Dataset on Traffic Volumes, Congestion Penalties, Tailpipe Emissions, and Commuter Economic Impacts along the Satellite Town Corridors of Yaoundé, Cameroon

This dataset supports the research article, “Functional Centralization, Congestion, and Tailpipe Emissions: Evidence from Yaoundé, Cameroon.” It provides harmonized, analysis-ready data on traffic activity, congestion penalties, tailpipe emissions, and commuter economic impacts along the three principal satellite town corridors linking Soa, Mfou, and Mbankomo to Yaoundé’s metropolitan core.

The dataset integrates three complementary components collected between January and March 2025: (1) a roadside traffic and driver survey capturing hourly vehicle counts (by corridor and direction) using manual tallies and GPS time stamps across predefined time slots (06:00–07:00 through 19:00–22:00) over a full week; (2) a household survey of commuters residing in Soa, Mfou, and Mbankomo, covering demographics (age, gender, marital status, employment, income), one‑day travel diaries (trip origins/destinations, modes, purposes, durations), and expenditures (monthly transport spending and cost per trip); and (3) derived congestion and emissions indicators, including Stretch_Duration (minutes), Congestion_Penalty (minutes above free‑flow), and CO₂ estimates under multiple technology/operational tiers (e.g., Tier 1 baseline, Tier 1 + Delay, Tier 2), alongside aggregated exposure and cost metrics.

Emissions are computed by combining observed volumes, typical vehicle characteristics reported by drivers, and slot‑level speed/penalty profiles to estimate fuel use and CO₂. Congestion metrics summarize peak (07:00–10:00; 15:00–19:00), off‑peak (10:00–15:00), and evening (19:00–22:00) conditions for weekdays and weekends. Economic impact variables quantify commuter costs (cost per trip, monthly spending) and enable estimation of the Value of Time (VoT) via a multinomial logit (MNL) mode‑choice framework using trip duration, cost per trip, and income.

Data were digitized using KoboToolbox with double‑entry validation and screened for anomalies. Sample targets were derived from the municipal census (2021) using Yamane’s approach for simple random sampling; due to field constraints, the realized household sample represents approximately 41.35% of the target, with surveys conducted on both weekdays and weekends under informed consent and anonymity. Traffic counts span a continuous seven‑day cycle for each corridor.

This dataset enables replication of the study’s findings and supports further research on urban transport centralization, congestion dynamics, emissions accounting, and policy analysis for rapidly growing african cities.