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

By Jameson L. Toole, Serdar Çolak, Bradley Sturt, Lauren P. Alexander, A. Evsukoff, Marta C. González. 2015

414 citations recorded by the source corpus.

Abstract 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.

Subfields: Transportation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Transportation

Citations in the corpus

A survey of data fusion in smart city applications (2019, background): “…most common subjects of monitoring and understanding human mobility include distance and duration distributions [191], origin-destination matrices [103], individual activity-based mobility patterns [192], transportation mode identiﬁcation [54], and densities and ﬂows within a building (or a…”
Big Data and cloud computing: innovation opportunities and challenges (2017, background): “Toole et al. (2015) presented a flexible, modular and computationally efficient software system to transform and integrate raw, massive data of call detail records (CDRs) from mobile phones into estimates of travel demand.”
Big Data Analytics in Intelligent Transportation Systems: A Survey (2019, methodology): “[118] use mobile phone data from open source data repositories to implement”
The TimeGeo modeling framework for urban mobility without travel surveys (2016, background): “By labeling visited location types for individual users as home, work, or other, representative traffic origin–destination (OD) matrices for an average day and by time of day can be generated (24, 25).”
Origin-destination trips by purpose and time of day inferred from mobile phone data (2015, methodology): “We compare the detected groups with the ones extracted via the patients ’ strati ﬁ cation technique to analyze the health records 24 .”
Inferring Passenger Travel Demand to Improve Urban Mobility in Developing Countries Using Cell Phone Data: A Case Study of Senegal (2016, background): “Toole et al. [25] integrated cellphone data with census, surveys, open and crowd-sourced geospatial data to estimate multiple aspects of travel demand that include road network performance and road classification analysis.”
The governance of smart mobility (2017, background): “…user-centred information which is context specific and integrates mobility and non-mobility options, which draws upon crowd-sourced, real-time data (see Toole et al., 2015); • Increasingly ‘intelligent’ infrastructure, including connected vehicles, which derives operational information from users…”
Graph Neural Networks for Intelligent Transportation Systems: A Survey (2023, background): “Traditionally, travel demand forecasting was almost done for mid-term or long-term decision-making and transportation systems developments [44].”
A Survey on Trajectory Data Management, Analytics, and Learning (2020, background): “te planning by first clustering all points in taxi trajectories to determine “hot spots” that could be bus stops, and then created bus routes based on the connectivity between two stops. Toole et al. [157] used census records and mobile phone location information to estimate demand in transit routes”
The promises of big data and small data for travel behavior (aka human mobility) analysis (2016, methodology): “A more advanced scaling method involves the calculation of the scaling factors together with the traffic assignment step, either iteratively (Ma et al., 2012) or as an optimization problem (Iqbal et al., 2014; Toole et al., 2015).”
Human mobility: Models and applications (2017, methodology): “Several algorithms in the literature are reviewed in [98], and further successive refinements and improvements in the methodology have been made since [95, 90, 100, 97].”