Role of Urban Big Data in Travel Behavior Research

Abstract

Understanding urban travel behavior (TB) is critical for advancing urban transportation planning practice and scholarship; however, traditional survey data is expensive (because of labor costs) and error-prone. With advances in data collection techniques and data analytic approaches, urban big data (UBD) is currently generated at an unprecedented scale in relation to volume, variety, and speed, producing new possibilities for applying UBD for TB research. A review of more than 50 scholarly articles confirms the remarkable and expanding role of UBD in TB research and its advantages over traditional survey data. Using this body of published work, a typology is developed of four key types of UBD—social media, GPS log, mobile phone/location-based service, and smart card—focusing on the features and applications of each type in the context of TB research. This paper discusses in significant detail the opportunities and challenges in the use of UBD from three perspectives: conceptual, methodological, and political. The paper concludes with recommendations for researchers to develop data science knowledge and programming skills for analysis of UBD, for public and private sector agencies to cooperate on the collection and sharing of UBD, and for legislators to enforce data security and confidentiality. UBD offers both researchers and practitioners opportunities to capture urban phenomena and deepen knowledge about the TB of individuals.

The built environment, bus routes and bus ridership: multi-level analysis for the Buffalo area

“…Higher population and employment densities, lower unemployment rate, lower household income, and lower rate of the vehicle ownership will all lead to higher public transit users and trips (Taylor & Fink, 2003;Cervero et al, 2010;Guerra & Cervero, 2011;Chakraborty & Mishra, 2013;Duduta, 2013;Zhao et al, 2013;Ma et al, 2018). Spatial factors and built environment factors, such as walking service areas overlapped with bus stops (Kimpel et al, 2007), catchment areas of bus stops (Zhao et al, 2013), residential density, land-use mix (LUM), and rail or vehicle accessibility (Liu & Shen, 2011;Sung & Oh, 2011;Ma et al, 2018;Kang et al, 2020), also play a significant role in transit ridership analysis (Chakraborty & Mishra, 2013;Miller et al, 2018;Wang & Hess, 2020). Additionally, other factors, such as weather (Zhou et al, 2017;Wei, 2022), dissemination of the real-time information (Brakewood et al, 2015), financial support (Cervero, 2013), and bus stop amenities (Shi et al, 2021), can influence passenger travel behavior or social equity in public transit, and hence, ultimately influence the ridership.…”

“…Lower fares and higher public transit service frequency could attract more transit riders (Balcombe et al, 2004;Guerra & Cervero, 2011;Brechan, 2017). Additionally, the role of better service and coordination could attract more public transit ridership (Taylor & Fink, 2003;Brown & Thompson, 2008b;Guerra & Cervero, 2011;Walker, 2012;Dill et al, 2013;Van Lierop et al, 2018;Diab et al, 2020;Wang & Hess, 2020). Also, park-and-ride facilities are considered a complementary component for rail service, attracting more transit users (Duncan, 2010;Dill et al, 2013).…”

Big Data Analysis for Travel Time Characterization in Public Transportation Systems

“…The analysis of big data from urban sources has been applied in many works oriented towards characterizing the mobility behavior of citizens, as well as to evaluate the efficacy of the service provided by public transportation systems [23,24]. The application of big data analysis for studying public transportation systems has been presented in reviews by Zheng et al [25] and Welch et al [26].…”

Rekayasa Model Perencanaan Transportasi dalam Menciptakan Mobilitas yang Aman pada Masa Pandemi Covid 19 (Studi Kasus : Mobilitas Mahasiswa di Jawa Timur)

“…Oleh karena sebab tersebut, terjadi penurunan mobilitas, di semua lokasi transit seperti stasiun, tempat kerja, ritel serta rekreasi (Pepe et al, 2020), demikian pula pada volume lalu lintas berkurang secara signifikan karena pandemi (Vingilis et al, 2020;Katrakazas et al, 2020). Dapat diartikan tahapan trip production terdampak secara signifikan terutama pada penggunaan angkutan umum di kota-kota padat penduduk (Ghosh et al, 2020); seperti penurunan ratarata penumpang kereta bawah tanah (Teixeira & Lopes, 2020;Wang et al, 2020) dan penurunan permintaan kereta komuter (https://www.urban-transport-magazine.com/en/decline-in-ridership-adapted-timetables-and-disinfectionrobots-the-impact-of-corona-Covid-10-on-public-transport/ diakses tanggal 20 Maret 2021). Sementara itu trip distribution pada kendaraan pribadi menjadi bertambah dengan pesat, karena masyarakat mengubah pola perjalanan lokalnya menjadi transportasi pribadi pada periode pasca-pandemi (Koehl, 2020;Laverty et al, 2020).…”