BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//pretalx//talks.osgeo.org//foss4g-2026//talk//3ES8DP
BEGIN:VTIMEZONE
TZID:JST
BEGIN:STANDARD
DTSTART:20000101T000000
RRULE:FREQ=YEARLY;BYMONTH=1
TZNAME:JST
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
UID:pretalx-foss4g-2026-3ES8DP@talks.osgeo.org
DTSTART;TZID=JST:20260902T143000
DTEND;TZID=JST:20260902T150000
DESCRIPTION:Urban traffic congestion costs Southeast Asian economies 2–5%
  of GDP annually\, yet the analytical methods needed to disentangle its sp
 atial and temporal drivers—exploratory spatial statistics\, network topo
 logy analysis\, multilevel variance decomposition—have traditionally req
 uired proprietary GIS platforms whose licensing fees are prohibitive for t
 he developing-country cities that need them most. This extended abstract p
 resents traffic-congestion-pipeline\, an open-source Python package (v0.4.
 0\, MIT license\, pip install traffic-congestion-pipeline) that delivers a
  complete\, reproducible workflow for large-scale spatiotemporal traffic r
 esearch using exclusively FOSS4G tools. The package\, its source code\, an
 d full API documentation are publicly available at https://github.com/firm
 anhadi21/traffic-analyses and https://firmanhadi21.github.io/traffic-analy
 ses/.\n\nThe pipeline integrates five mature open-source components into a
  unified eleven-command CLI and Python API. (1) PySAL's esda and libpysal 
 compute Global Moran's I and Local Indicators of Spatial Association (LISA
 ) with K-nearest-neighbor spatial weights for hotspot detection. (2) PySAL
 's giddy module fits classic Markov and Spatial Markov transition models t
 o quantify how congestion hotspots persist over time and whether their tra
 nsitions depend on neighboring segments' states (spatial contagion). (3) O
 SMnx downloads street network graphs and computes betweenness centrality a
 nd graph-based capacity-drop detection\, linking network topology to conge
 stion. (4) statsmodels.mixedlm fits nested mixed-effects models—null\, t
 emporal\, and full—that rigorously partition within-segment (temporal) a
 nd between-segment (spatial) variance using absolute speed (km/h) rather t
 han the normalized jam factor\, avoiding circularity from free-flow speed 
 normalization. (5) Uber H3 hexagonal aggregation re-runs spatial autocorre
 lation tests at multiple resolutions (6–9) to check robustness against t
 he modifiable areal unit problem (MAUP). Each component maps to a dedicate
 d CLI command (traffic-pipeline multilevel\, markov\, speed-validation\, h
 3-robustness\, etc.)\, allowing researchers to execute any stage independe
 ntly or chain the full workflow from automated data collection to publicat
 ion-ready figures.\n\nWe apply this pipeline to over 264 million traffic o
 bservations collected at 15-minute intervals from the HERE Traffic API acr
 oss three Indonesian cities over 11 months (March 2025–February 2026): J
 akarta (14\,549 road segments\, population 10.5 million)\, Bandung (3\,069
  segments\, 2.5 million)\, and Semarang (1\,076 segments\, 1.8 million). T
 hese cities span a 20× population range and feature high motorcycle mode 
 shares\, representing traffic dynamics markedly different from car-dominat
 ed Western cities.\n\nMultilevel variance decomposition shows that 88–89
 % of total speed variance lies between segments (ICC)\, reflecting road de
 sign differences. Within segments\, time-of-day explains 57–67% of speed
  fluctuations\, while betweenness centrality adds less than 1% explanatory
  power beyond road type. ANOVA across four speed metrics confirms this tem
 poral dominance is not a normalization artifact: η² = 8–10% for jam fa
 ctor and speed reduction\, 5–6% for absolute speed\, and effectively zer
 o for free-flow speed. Centrality correlations with absolute current speed
  are near zero (R² < 0.003)\; moderate centrality–jam factor correlatio
 ns (R² = 0.06–0.14) are mediated entirely through free-flow speed. Even
 ing peak congestion exceeds daily averages by approximately 40% across all
  three cities.\n\nGlobal Moran's I is non-significant for all cities (p > 
 0.35). LISA identifies local clusters in ~10% of segments along known corr
 idors\, though none survive FDR correction. LISA Markov analysis reveals a
 n inverse relationship between city size and hotspot persistence: Semarang
  retains hotspots at 18.8% probability versus Jakarta's 6.5%\, yet no segm
 ent persists across all eight daily periods. Spatial Markov testing detect
 s significant contagion in Bandung (χ² = 8.43\, p = 0.004) and Semarang 
 (χ² = 6.48\, p = 0.011)\, but not in Jakarta\, where denser network topo
 logy dissipates spillovers. H3 robustness analysis confirms null spatial a
 utocorrelation at neighbourhood scales for Bandung and Semarang\, while re
 vealing a weak signal for Jakarta at resolution 8 (I = 0.030\, p = 0.033).
 \n\nThis work is relevant to the FOSS4G community for four reasons. First\
 , it demonstrates that PySAL\, OSMnx\, statsmodels\, and H3—all open-sou
 rce—can handle a dataset of 264 million observations end-to-end in appro
 ximately 15 minutes on consumer hardware (Mac Mini M2 Pro)\, establishing 
 that FOSS4G tools are ready for operational\, city-scale traffic analysis\
 , not just prototyping. Second\, the pipeline is released as a versioned\,
  installable PyPI package with eleven CLI commands and comprehensive docum
 entation\, lowering the barrier for adoption by transportation agencies in
  resource-constrained settings where proprietary licenses are unaffordable
 . Third\, the modular\, command-per-analysis architecture provides a reusa
 ble template for applying FOSS4G tools to other urban analytics domains—
 air quality\, land use change\, public health surveillance—extending the
  impact beyond traffic. Fourth\, the substantive finding—that congestion
  is primarily a demand synchronization problem rather than a spatial infra
 structure constraint—carries direct policy implications for cities inves
 ting in road expansion versus demand management\, and was only discoverabl
 e through the multi-method integration that Python's composable ecosystem 
 uniquely enables.\n\nThe convergence of four independent spatial tests on 
 null results for absolute speed\, validated through MAUP-robust H3 aggrega
 tion\, provides a level of methodological rigor that strengthens confidenc
 e in this conclusion. We believe this combination of open-source tooling\,
  reproducible packaging\, large-scale empirical validation\, and policy-re
 levant findings makes a strong case for presentation at the FOSS4G Academi
 c Track\, and we look forward to engaging with the community on extending 
 the pipeline to cities beyond Indonesia.\n\nKeywords: open-source\, reprod
 ucibility\, PySAL\, OSMnx\, traffic congestion\, FOSS4G
DTSTAMP:20260717T225804Z
LOCATION:Cosmos2
SUMMARY:Open-Source Spatiotemporal Traffic Congestion Analysis at City Scal
 e: A Reproducible Python Pipeline Combining PySAL\, OSMnx\, and Multilevel
  Modeling - Firman Hadi\, Fauzan Abdullah
URL:https://talks.osgeo.org/foss4g-2026/talk/3ES8DP/
END:VEVENT
END:VCALENDAR