Human mobility: Models and applications

Using an algorithm to analyze opportunistically collected mobile phone location data, the authors estimate weekday and weekend travel patterns of a large metropolitan area with high accuracy.

/pdf/estimating-origin-destination-flows-using-mobile-phone-4nsmyjt5xx.pdf

Estimating Origin-Destination Flows Using Mobile Phone Location Data

In this research, we propose a methodology to develop OD matrices using mobile phone Call Detail Records (CDR) and limited traffic counts. CDR, which consist of time stamped tower locations with caller IDs, are analyzed first and trips occurring within certain time windows are used to generate tower-to-tower transient OD matrices for different time periods. These are then associated with corresponding nodes of the traffic network and converted to node-to-node transient OD matrices. The actual OD matrices are derived by scaling up these node-to-node transient OD matrices. An optimization based approach, in conjunction with a microscopic traffic simulation platform, is used to determine the scaling factors that result best matches with the observed traffic counts. The methodology is demonstrated using CDR from 2.87 million users of Dhaka, Bangladesh over a month and traffic counts from 13 key locations over 3 days of that month. The applicability of the methodology is supported by a validation study.

/pdf/development-of-origin-destination-matrices-using-mobile-1kneil10uc.pdf

Development of origin–destination matrices using mobile phone call data

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

Acquiring high-quality origin-destination (OD) information for traffic in a geographic area is both time consuming and expensive while using conventional methods such as household surveys or roadside monitoring. These methods generally present only a snapshot of traffic situation at a certain point in time, and they are updated in time intervals of up to several years. A technique was developed that makes use of the global system for mobile communications (GSM) mobile phone network. Instead of monitoring the flow of vehicles in a transportation network, the flow of mobile phones in a cell-phone network is measured and correlated to traffic flow. This methodology is based on the fact that a mobile phone moving on a specific route always tends to change the base station nearly at the same position. For a first pilot study, a GSM network simulator has been designed, where network data can be simulated, which is then extracted from the phone network, correlated, processed mathematically and converted into an OD matrix. Primary results show that the method has great potential, and the results inferred are much more cost-effective than those generated with traditional techniques. This is due to the fact that no change has to be made in the GSM network, because the information that is needed can readily be extracted from the base station database, that is the entire infrastructure needed is already in place

http://www.esi2.us.es/GT/docs/iet_art1.pdf

Deriving origin destination data from a mobile phone network

One of the main characteristics of modern society is the never-ending increase in mobility. This leads to a series of problems such as congestion and increased pollution. To resolve these problems, it is imperative to have a good road network management and planning. To efficiently identify the characteristics of traffic in the road network, it would be necessary to perform a permanent monitorisation of all roadway links. This would involve an excessive cost of installation and maintenance of road infrastructure. Hence, new alternatives are required which can characterise traffic in a real time with good accuracy at an acceptable price. Mobile telephone systems are considered as a promising technology for the traffic data collection system. Its extensive use in converting its subscribers in a broad sample to draw information from phones becomes anonymous probes to monitor traffic. It is reviewed how to obtain parameters related to traffic from cellular-network-based data, describing methods used in existing simulation works as well as field tests in the academic and industrial field.

Review of traffic data estimations extracted from cellular networks

This paper introduces a methodology for the identification of events or potentially dangerous situations in road traffic by extracting information from the OBD-II interface. The proposed system for identifying hazardous locations includes a GNSS positioning system. As such, with the use of existing navigation systems or with the use of newly installed equipment, the exact geographic location where a given risk occurs will be identified.

Johan Wideberg

Papers

Deriving origin destination data from a mobile phone network

Review of traffic data estimations extracted from cellular networks

A smartphone application to extract safety and environmental related information from the OBD-II interface of a car

Fully optimized energy management for propulsion, thermal cooling and auxiliaries of a serial hybrid electric vehicle

Development and Experimental Validation of a Dynamic Model for Electric Vehicle with in Hub Motors