Crowdsourcing Real-Time Traveler Information Systems

In the last decade, the concept of collecting traffic data using location aware and data enabled smartphones in place of traditional sensor networks has received much attention. With a steady market growth for smartphones enabled with GPS chipsets, the potential of this technology is enormous. This combined with the pervasion of participatory paradigms such as crowdsourcing wherein individuals with portable sensors instead of physical networks serve as sensors providing information. Crowd sensed data overcome a number of issues with traditional physical sensor networks by providing wider coverage, real-time data, data redundancy and cost effectiveness to name a few. While there has been a lot of work on actual implementations of crowd sensed traffic monitoring programs, there is limited work on assessing the quality, and validity of crowd sensed data. A systematic analysis of quality and validity is needed before this paradigm can be more commonly adopted for traffic monitoring applications. To this end, research is underway to deploy a crowdsourced platform for monitoring and providing real-time transit information for shuttles that serve the University of Connecticut. The thesis develops a framework and an open-source prototype system that is able to produce real-time traveler information based on crowdsourced data. In order to build the prototype, first it implements a robust Hidden Markov Model based map-matching algorithm to position the crowdsourced data on the underlying road network and retrieve the likely path. The accuracy of the map-matching algorithm has been found satisfactory for the current usage even when the GPS points are sampled at low frequency. Next, to predict the travel condition across the network from the crowdsourced data, a travel time prediction algorithm, based on Regularized Least Square Regression, has been implemented as well. This travel time prediction algorithm, together with the map-matching algorithm, has been applied in a simulated crowdsourcing environment. The travel time prediction results of the simulation show that the prototype system is quite capable of predicting travel time even when the crowdsourced real-time data is sparse. The simulation tests the performance of the travel time prediction algorithm in different scenarios. From the demonstrated predictive performance of the implemented prototype system, this approach to providing real-time traveler information is found promising. It is also possible to apply the prototype to all regions and all modes of transportation, exploiting its generalized approach of providing real-time traveler information from crowdsourced data

Optimising the Loading Diversity of Rail Passenger Crowding using On-Board Occupancy Data

Crowded conditions on trains can lead to lower passenger satisfaction, discourage rail travel, result in negative economic impacts and are a factor in a number of health and safety hazards. In the UK there is an annual survey of rail passenger crowding, although the measures used do not reflect coach-by-coach variations, nor do they reflect variations across the peak period. In this MPhil thesis I investigated the application of weight-based automatic passenger counting data to deliver more even loadings on trains through the provision of new real-time and static solutions. In addition I investigated the potential benefits of such solutions in terms of reduced dwell times and reduced crowding. The overall concept proposed was to make the most of the existing available capacity; for example, so that no-one is standing when seats are available. Through analysing a large sample of air suspension data, I identified station-specific trends where some coaches were over capacity while others had spare capacity. I also conducted a critical review of academic research into on-train crowding and solutions that seek to optimise ‘loading diversity’. This study contributes to this emerging subject area in several ways: I propose two new metrics to describe inter-coach loading diversity that, unlike existing metrics, contain information relative to the capacity; I have revealed a link between the inter-coach loading diversity metrics and estimated boarding times, with trains classified as ‘very uneven’ on departure typically having dwell times of approximately five to ten seconds greater than services that were classified as being ‘even’ with a similar total number of passengers on board; and finally I have applied classification supervised learning techniques to predict the load factor for a given service and these predictors were an improvement over taking the historical average

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

Crowdsourcing traffic data for travel time estimation

Travel time estimation is a fundamental measure used in routing and navigation applications, in particular in emerging intelligent transportation systems (ITS). For example, many users may prefer the fastest route to their destination and would rely on real-time predicted travel times. It also helps real-time traffic management and traffic light control. Accurate estimation of travel time requires collecting a lot of real-time data from road networks. This data can be collected using a wide variety of sources like inductive loop detectors, video cameras, radio frequency identification (RFID) transponders etc. But these systems include deployment of infrastructure which has some limitations and drawbacks. The main drawbacks in these modes are the high cost and the high probability of error caused by prevalence of equipment malfunctions and in the case of sensor based methods, the problem of spatial coverage.;As an alternative to traditional way of collecting data using expensive equipment, development of cellular & mobile technology allows for leveraging embedded GPS sensors in smartphones carried by millions of road users. Crowd-sourcing GPS data will allow building traffic monitoring systems that utilize this opportunity for the purpose of accurate and real-time prediction of traffic measures. However, the effectiveness of these systems have not yet been proven or shown in real applications. In this thesis, we study some of the current available data sets and identify the requirements for accurate prediction. In our work, we propose the design for a crowd-sourcing traffic application, including an android-based mobile client and a server architecture. We also develop map-matching method. More importantly, we present prediction methods using machine learning techniques such as support vector regression.;Machine learning provides an alternative to traditional statistical method such as using averaged historic data for estimation of travel time. Machine Learning techniques played a key role in estimation in the last two decades. They are proved by providing better accuracy in estimation and in classification. However, employing a machine learning technique in any application requires creative modeling of the system and its sensory data. In this thesis, we model the road network as a graph and train different models for different links on the road. Modeling a road network as graph with nodes and links enables the learner to capture patterns occurring on each segment of road, thereby providing better accuracy. To evaluate the prediction models, we use three sets of data out of which two sets are collected using mobile probing and one set is generated using VISSIM traffic simulator. The results show that crowdsourcing is only more accurate than traditional statistical methods if the input values for input data are very close to the actual values. In particular, when speed of vehicles on a link are concerned, we need to provide the machine learning model with data that is only few minutes old; using average speed of vehicles, for example from the past half hour, as is usually seen in many web based traffic information sources may not allow for better performance

Sociotechnical co-production of planning information : opportunities and limits of crowdsourcing for the geography and planning of bicycle transportation

Urban planners deploy civic technologies to engage publics with digital tools in a relative vacuum of theory, understanding of challenges, or benefits. The issue, Lewis Mumford might have framed, could be of authoritarian and democratic technics—whether the technology contributes more to top-down control or bottom-up understanding. Building from collaborative planning theory, co-production suggests ways people can leverage technologies to build urban solutions with or without professional planners. Empirical research shows that crowdsourcing to address planning questions with digital civic platforms can help fill or mitigate information gaps, including support for bicycling as a safe and comfortable travel mode. However, no research has addressed how crowdsourced information for bicycle planning offers new insights for safety, the geography of participation, or how its social construction impacts its representation of bicycling in a community. A new framework for evaluating co-productive planning is proposed, considering legitimacy, accessibility, social learning, transparency, and representation (LASTR). This dissertation addresses these concerns of safety, geography, and social construction through the LASTR framework using mixed-methods case studies in Portland, Oregon, and Austin, Texas. Bicycle volumes and street ratings through the crowdsourcing platform, along with geographic information system environmental data, and interviews with thirty-three informants form the basis for evaluating these issues. Viewed from pragmatism and social construction of technology, the social processes of planning and technological developments are intertwined and traced in tandem. The first three chapters frame the problems, build a background in theory, and describe the research questions, planning contexts, and data for analysis. The next three chapters are empirical, evaluating the use of crowdsourced information for bicycle safety, comparing the geography of crowdsourced participation with in-person meetings from both cities’ most recent bicycle planning process, and tracing the sociotechnical representation of crowdsourcing bicyclist information through interviews and case materials. The final chapter summarizes the findings and implications for practice and research. This dissertation shows that the biased representation of bicycling in these two crowdsourcing cases pose opportunities to identify safer bicycling routes and expand public participation geographies, but could exacerbate problems with aligning public improvements with the users of a specific technological approach. Further, the construct of crowdsourcing for urban planning remains flexible and therefore merits further study and knowledge transfer for practitioners and students.Community and Regional Plannin

Assessing the effectiveness of crowdsourced geographic information for solid waste management in Timor-Leste : a thesis presented in partial fulfilment of the requirements for the degree of Master of Information Sciences (Information Technology) at Massey University, Albany, New Zealand

Dili, the capital city of Timor-Leste has been faced with serious solid waste problems in recent years. Responding to this issue, the government has adopted various policies including setting up solid waste collection sites in community areas and outsourcing collection to the private sector to collect waste directly from homes in several areas. Despite, these efforts, waste is still found scattered on the roads and disposed of in rivers and open lands. A proper solid waste management strategy is necessary to transform the city into a clean city. In order to develop an effective solid waste management strategy, reliable data and public participation are required. This study, therefore, investigated whether crowdsourcing, in particular, Volunteered Geographic Information (VGI) can effectively be used to collect data about solid waste disposal and collection practices in Dili and raise awareness of the impact of waste disposal practices among the public. The study result demonstrated that crowdsourcing is a viable method for collecting solid waste data. Challenges such as collecting accurate location-specific data still remain, hence, the crowdsourced dataset may not entirely substitute for the usual traditional dataset. At this stage, however, the collected data can still be utilized as a supplementary data source. In the future, by improving data collection methodologies, such as using smaller rewards or providing necessary facilities, a crowdsourcing-based data collection method could be utilized as an adequate substitute for traditional data source because of its ability to collect data in real- time with lower operational costs. This approach is feasible for a developing country such as Timor-Leste where critical area such as waste management has less priority for funding

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management