International Journal For Multidisciplinary Research

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Volume 8 Issue 4
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Calibrated Arrival-Time Prediction for Indian Road Freight

Author(s) Mr. Saurabh Kumar
Country India
Abstract Routing engines predict how long it takes to drive a road. For Indian long-haul
freight they are wrong by a factor of three to four, because a vehicle is not
driving most of the time. We present an arrival-time engine assembled from three
measured layers: a per-road-class speed correction derived from map-matched fleet
traces, a stop model derived from the observed distribution of halt durations, and
a weather layer combining published speed-reduction coefficients with two locally
trained models. The output is a calibrated arrival distribution rather than a point
estimate.

Evaluated on 790 held-out journeys under a strict temporal split, the engine
achieves a median absolute error of 2.85 h and a median absolute percentage error
of 26.9%, against 7.79 h and 64.9% for the incumbent telematics estimate in
production on the same fleet, and 3.50 h and 30.3% for a well-fitted constant-speed
baseline. Nominal 50%, 80% and 90% quantiles achieve 44%, 77% and 88% empirical
coverage.

We report five hypotheses that were measured and rejected, two of which were our
own models that passed conventional validation while being materially broken. The
most consequential result is methodological: the definition of arrival in
commercial telematics data is unreliable enough to invalidate model evaluation, and
a behaviour-derived definition is required before any accuracy figure can be
trusted.
Keywords arrival time estimation, travel time prediction, road freight, fleet telematics, quantile regression, probabilistic forecasting, weather impact on traffic, model calibration, India
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 4, July-August 2026
Published On 2026-08-16
DOI https://doi.org/10.36948/ijfmr.2026.v08i04.86012
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CrossRef DOI prefix of IJFMR is 10.36948/ijfmr

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