Human mobility: Models and applications

Big data is becoming a research focus in intelligent transportation systems (ITS), which can be seen in many projects around the world. Intelligent transportation systems will produce a large amount of data. The produced big data will have profound impacts on the design and application of intelligent transportation systems, which makes ITS safer, more efficient, and profitable. Studying big data analytics in ITS is a flourishing field. This paper first reviews the history and characteristics of big data and intelligent transportation systems. The framework of conducting big data analytics in ITS is discussed next, where the data source and collection methods, data analytics methods and platforms, and big data analytics application categories are summarized. Several case studies of big data analytics applications in intelligent transportation systems, including road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plan, rail transportation management and control, and assets maintenance are introduced. Finally, this paper discusses some open challenges of using big data analytics in ITS.

Big Data Analytics in Intelligent Transportation Systems: A Survey

The last decade has witnessed very active development in two broad, but separate fields, both involving understanding and modeling of how individuals move in time and space (hereafter called "travel behavior analysis" or "human mobility analysis"). One field comprises transportation researchers who have been working in the field for decades and the other involves new comers from a wide range of disciplines, but primarily computer scientists and physicists. Researchers in these two fields work with different datasets, apply different methodologies, and answer different but overlapping questions. It is our view that there is much, hidden synergy between the two fields that needs to be brought out. It is thus the purpose of this paper to introduce datasets, concepts, knowledge and methods used in these two fields, and most importantly raise cross-discipline ideas for conversations and collaborations between the two. It is our hope that this paper will stimulate many future cross-cutting studies that involve researchers from both fields.

The promises of big data and small data for travel behavior (aka human mobility) analysis.

In this study, with Singapore as an example, we demonstrate how we can use mobile phone call detail record (CDR) data, which contains millions of anonymous users, to extract individual mobility networks comparable to the activity-based approach. Such an approach is widely used in the transportation planning practice to develop urban micro simulations of individual daily activities and travel; yet it depends highly on detailed travel survey data to capture individual activity-based behavior. We provide an innovative data mining framework that synthesizes the state-of-the-art techniques in extracting mobility patterns from raw mobile phone CDR data, and design a pipeline that can translate the massive and passive mobile phone records to meaningful spatial human mobility patterns readily interpretable for urban and transportation planning purposes. With growing ubiquitous mobile sensing, and shrinking labor and fiscal resources in the public sector globally, the method presented in this research can be used as a low-cost alternative for transportation and planning agencies to understand the human activity patterns in cities, and provide targeted plans for future sustainable development.

http://www2.cs.uic.edu/~urbcomp2013/urbcomp2015/papers/Human-Mobility-Inference_Jiang.pdf

Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore

Rapid urbanization is placing increasing stress on already burdened transportation infrastructure. Ubiquitous mobile computing and the massive data it generates presents new opportunities to measure the demand for this infrastructure, diagnose problems, and plan for the future. However, before these benefits can be realized, methods and models must be updated to integrate these new data sources into existing urban and transportation planning frameworks for estimating travel demand and infrastructure usage. While recent work has made great progress extracting valid and useful measurements from new data resources, few present end-to-end solutions that transform and integrate raw, massive data into estimates of travel demand and infrastructure performance. Here we present a flexible, modular, and computationally efficient software system to fill this gap. Our system estimates multiple aspects of travel demand using call detail records (CDRs) from mobile phones in conjunction with open- and crowdsourced geospatial data, census records, and surveys. We bring together numerous existing and new algorithms to generate representative origin–destination matrices, route trips through road networks constructed using open and crowd-sourced data repositories, and perform analytics on the system’s output. We also present an online, interactive visualization platform to communicate these results to researchers, policy makers, and the public. We demonstrate the flexibility of this system by performing analyses on multiple cities around the globe. We hope this work will serve as unified and comprehensive guide to integrating new big data resources into customary transportation demand modeling.

The path most traveled: Travel demand estimation using big data resources

In this work, we present methods to estimate average daily origin–destination trips from triangulated mobile phone records of millions of anonymized users. These records are first converted into clustered locations at which users engage in activities for an observed duration. These locations are inferred to be home, work, or other depending on observation frequency, day of week, and time of day, and represent a user’s origins and destinations. Since the arrival time and duration at these locations reflect the observed (based on phone usage) rather than true arrival time and duration of a user, we probabilistically infer departure time using survey data on trips in major US cities. Trips are then constructed for each user between two consecutive observations in a day. These trips are multiplied by expansion factors based on the population of a user’s home Census Tract and divided by the number of days on which we observed the user, distilling average daily trips. Aggregating individuals’ daily trips by Census Tract pair, hour of the day, and trip purpose results in trip matrices that form the basis for much of the analysis and modeling that inform transportation planning and investments. The applicability of the proposed methodology is supported by validation against the temporal and spatial distributions of trips reported in local and national surveys.

Origin-destination trips by purpose and time of day inferred from mobile phone data

Traffic congestion has become a threat to many U.S. cities, including the City of Chicago. As a promising alternative, Bus Rapid Transit (BRT) may improve level-of-service (LOS) of the bus network; however, the real challenge addressed in this paper is how to evaluate the impacts of such policies on different stakeholders (i.e., auto-drivers and bus-riders) prior to implementation. This study relies on the preparation of a microscopic traffic simulation model for the Chicago Loop area, aided by the utilization of a geographic information system (GIS) traffic network, traffic counts, traffic signals and the Chicago Transit Authority (CTA) bus service data. This study proposes three sets of indicators for the evaluation of the proposed schemes: 1) bus travel speed, 2) automobile travel speed, and 3) bus travel time and reliability. These performance indicators will serve to compare the current base case to the proposed bus improvement scenarios. Based on the evaluation of three scenarios, this study provides recommendations on how to improve bus LOS in the Chicago Loop area.

Linking Planning and Operations for BRT: A Microscopic Traffic Simulation Study for the Chicago Loop

Replacement of hydraulic actuators used in flight control surfaces by electromechanical systems is a current ongoing topic that targets higher performance in terms of efficiency, weight, maintenance, and pollution. While hydraulic actuators are already well-known systems for flight control surfaces, electromechanical actuators are still being developed and improved, and they have to be rigorously tested before they are used in the sky. Test benches used to analyze such systems are designed to be specific to the actuator being tested and lack correlation with other aircraft actuators. This work proposes a modular and flexible test rig architecture that allows different actuators to be tested alone or in combination so that all actuators of both wings may be analyzed together. Moreover, the proposed system allows the actuators to be tested by using dedicated power and control systems or by directly connecting them to the control bus bar and power source of a real aircraft. The test rig architecture and its components are described in detail and their application in new types of aileron and flap-tab electromechanical actuators explained.

Mikel Murga

Papers

Origin-destination trips by purpose and time of day inferred from mobile phone data

Linking Planning and Operations for BRT: A Microscopic Traffic Simulation Study for the Chicago Loop

Modular and Flexible On-Ground Test Bench for Aircraft Electromechanical Actuators