This study examines the determinants of commuting time and distance using the mobility situations framework in the Mexico City Metropolitan Zone (MCMZ), a megacity marked by spatial mismatch, socioeconomic segregation, and fragmented transport infrastructure. Using data from the 2017 Origin-Destination Survey, we classify 192 travel districts into four mobility situations—Short Commutes, Long Commutes, Travelscarps, and Wormholes—based on average commuting time and distance. Our approach combines spatial econometrics with a machine learning LASSO algorithm to evaluate 88 potential predictors across transport infrastructure, urban spatial structure, and socioeconomic conditions. Results show that each situation is driven by distinct factors: Short Commutes align with centrality and privilege; Long Commutes with exclusion and mass transit; Travelscarps with inefficient short trips from poor infrastructure; and Wormholes with efficient long trips through multimodal strategies. The study demonstrates the value of the mobility situations framework in a Global South city and highlights machine learning’s utility for variable selection and theory-building in journey-to-work research.
AnselinL (1988) Spatial Econometrics: Methods and Models. Dordrecht: Springer.
3.
AnselinL (2001). In: BaltagiBH (ed) Spatial Econometrics. Blackwell: A companion to theoretical econometrics.
4.
BakerDLópez-GarcíaD (2024) Do shifts in the racial and ethnic composition of a neighborhood lead to extreme changes in commuting times? Evidence from New York city. Travel Behaviour and Society36: 100775. https://doi.org/10.1016/j.tbs.2024.100775
5.
BastiaanssenJJohnsonDLucasK (2022) Does better job accessibility help people gain employment? The role of public transport in Great Britain. Urban Studies59(2): 301–322. https://doi.org/10.1177/00420980211012635
6.
Batista-DuarteLda MotaR (2023) The relevance of job accessibility to labour market outcomes: evidence for the São Paulo metropolitan region. Urban Studies60(16): 3233–3251.
7.
Bautista-HernándezDA (2020) Commuting inequality, role of urban structure, and identification of disadvantaged groups in the Mexico City metropolitan area. Journal of Transport and Land Use13(1): 159–183.
8.
Bautista-HernándezDA (2021) The urban form and the social dimension of commuting in México City. An individual trip-level analysis. Transportation Research Interdisciplinary Perspectives10: 100346.
9.
Bedoya-MayaFCalatayudAGonzálezV (2022) Estimating the effect of road congestion on air quality in Latin America. Transportation Research Part D113: 103510.
10.
BertaudA (2004) The spatial organization of cities: deliberate outcome or unforeseen consequence?UC Berkeley.IURD Working Paper Series.
11.
BertaudA (2018) Order Without Design: How Markets Shape Cities. Cambridge: The MIT Press.
12.
CalongeF (2018) Mobility resources and urban accessibility in municipalities in the south of the Guadalajara metropolitan area, México. Revista Urbano38: 48–57.
13.
CameronATrivediP (2022) Microeconometrics using stata In: Volume II: Nonlinear Models and Causal Inference Methods. 2nd edition. Stata Press.
14.
CerveroR (2013) Linking urban transport and land use in developing countries. Journal of Transport and Land Use6(1): 7–24. https://doi.org/10.5198/jtlu.v6i1.425
15.
CuiBBoisjolyGEl-GeneidyA, et al. (2019) Accessibility and the journey to work through the lens of equity. Journal of Transport Geography74: 269–277. https://doi.org/10.1016/j.jtrangeo.2018.12.003
16.
DeboosereREl-GeneidyA (2018) Evaluating equity and accessibility to jobs by public transport across Canada. Journal of Transport Geography73: 54–63. https://doi.org/10.1016/j.jtrangeo.2018.10.006
17.
FaizA (2011) Transportation and the urban poor. ITEA Journal81(12): 40–43.
18.
GarzaG (2020) Dinámica Y Configuración Macroeconómica De La Ciudad De México, 1960-2013: Estrategia económico-espacial Para La Planeación Multidimensional De La Ciudad De México 2020-2040. Santiago: Comisión Económica para América Latina y el Caribe.
GordonPRichardsonHWongH (1986) The distribution of population and employment in a polycentric city: the case of Los Angeles. Environment and Planning A: Economy and Space18(8): 161–173. https://doi.org/10.1068/a180161
22.
GrahamSMarvinS (2001) Splintering Urbanism: Networked Infrastructures, Technological Mobilities and the Urban Condition. New York and Abingdon: Routledge.
23.
HandySLNiemeierDA (1997) Measuring accessibility: an exploration of issues and alternatives. Environment and Planning A: Economy and Space29(7): 1175–1194. https://doi.org/10.1068/a291175
24.
HernándezDHanszMMassobrioR (2020) Job accessibility through public transport and unemployment in Latin America: the case of Montevideo (uruguay). Journal of Transport Geography85: 102742.
IbarraVNegreteMEGraizbordB (2016) Tres aristas de un mismo problema: Movilidad diferencial en la Ciudad de México. In: NegreteME (ed) Urbanización Y Política Urbana En Latino América: Experiencias, Análisis Y Reflexiones. Ciudad de México: El Colegio de México.
27.
ImmergluckD (1998) Job proximity and the urban employment problem: do suitable nearby jobs improve neighborhood employment rates?Urban Studies35(1): 7–23. https://doi.org/10.1080/0042098985041
LeSageJPaceR (2009) Spatial econometric models In: Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications. Springer Berlin Heidelberg.
30.
LevinsonDKumarA (1994) The rational locator: why travel times have remained stable. Journal of the American Planning Association60(3): 319–332. https://doi.org/10.1080/01944369408975590
31.
LiWZhongHBoarnetMG (2024) Effects of new transit lines on commuting: evidence from restricted-use census bureau microdata. Applied Geography164(1): 103202.
32.
López-GarcíaD (2023a) Desigualdades en el viaje al trabajo en la Zona Metropolitana de la Ciudad de México. In: SchetingartMSalazarCSobrinoJ (eds) Desigualdades Territoriales: Miradas Cruzadas. Ciudad de México: El Colegio de México.
33.
López-GarcíaD (2023b) Trayectorias residenciales y experiencia de movilidad en la Zona Metropolitana de Guadalajara. In: Espaciotecnología y acumulación (ed) La Construcción De Una Agenda De Investigación Para Los Estudios urbano-regionales. Buenos Aires: CEUR - Centro de Estudios Urbano Regionales.
34.
López-GarcíaD (2023c) Worker Mobility and Urban Policy in Latin America: Policy Interactions and Urban Outcomes in Mexico City. New York and Abingdon: Routledge.
35.
López-GarcíaD (2024) Diverging mobility situations in greater Mexico City: exploring the factors behind the mobility situations of public transport commuters. Urban Studies61(10): 1849–1870.
36.
López-GarcíaDBakerDM (2023) Diverging mobility situations: measuring relative job accessibility and differing socioeconomic conditions in New York city. Annals of the Association of American Geographers113(1): 149–168.
37.
ManaughKMiranda-MorenoLFEl-GeneidyAM (2010) The effect of neighbourhood characteristics, accessibility, home-work location, and demographics on commuting distances. Transportation37(4): 627–646. https://doi.org/10.1007/s11116-010-9275-z
McQuaidRWChenT (2012) Commuting times - the role of gender, children and part-time work. Research in Transportation Economics34(1): 66–73. https://doi.org/10.1016/j.retrec.2011.12.001
40.
MohinoIUrenaJMSolisE (2019) The influence of education level and job type on work-related travel patterns within rural metro-adjacent regions: the case of castilla-la Mancha, Spain. Journal of Transport and Land Use12(1): 73–98. https://doi.org/10.5198/jtlu.2019.1219
41.
MontejanoJCaudilloCSilvánJ (2016) Contesting Mexico City’s alleged polycentric condition through a centrality-mixed land-use composite index. Urban Studies53(11): 2380–2396.
42.
NegreteMEPaquetteC (2011) La interacción entre transporte público y urbanización en la Zona Metropolitana de la Ciudad de México: un modelo expansivo que llega a sus límites. Territorios25: 13–33.
43.
NiedzielskiMABoschmannEE (2014) Travel time and distance as relative accessibility in the journey to work. Annals of the Association of American Geographers104(6): 1156–1182. https://doi.org/10.1080/00045608.2014.958398
44.
PáezAScottDMMorencyC (2012) Measuring accessibility: positive and normative implementations of various accessibility indicators. Journal of Transport Geography25: 141–153.
45.
ReyesA (2023) Housing and transportation: the relationship between residential location, local retail economies, and commutes of low-income families in Mexico City. Journal of Planning Education and Research43(4): 1020–1030. https://doi.org/10.1177/0739456x20932983
46.
SchteingartMIbarraV (2016) Desarrollo urbano-ambiental Y Movilidad En La Ciudad De México: Evaluación Histórica, Cambios Recientes Y Políticas Públicas. Ciudad de México: El Colegio de México.
SongBMcDonaldJF (2003) Determinants of commuting time and distance for Seoul residents: the impact of family status on the commuting of women. Urban Studies40(7): 1283–1302.
50.
SuárezMDelgadoJ (2009) Is Mexico City polycentric? A trip attraction capacity approach. Urban Studies46(10): 2187–2211.
51.
SuárezMMurataMDelgadoJ (2016) Why do the poor travel less? Urban structure, commuting and economic informality in Mexico City. Urban Studies53(12): 2548–2566.
52.
SusiloYOMaatK (2007) The influence of built environment to the trends in commuting journeys in the Netherlands. Transportation34(5): 589–609. https://doi.org/10.1007/s11116-007-9129-5
53.
VandersmissenMHVilleneuvePThériaultM (2003) Analyzing changes in urban form and commuting time. The Professional Geographer55(4): 446–463.
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