🤖 AI Summary
This study addresses the challenge of achieving both accuracy and engineering practicality in travel time prediction under low-congestion conditions. The authors propose a lightweight modeling framework that integrates open-source geographic information with sparse traffic operational features—such as traffic signals, stop signs, and turn types—to generate shortest travel-time paths using Dijkstra’s algorithm, followed by path deviation correction via random forest regression. Notably, the method operates without real-time congestion data and delivers highly accurate and efficient point-to-point travel time estimates at an urban scale. Experimental results demonstrate that the model significantly outperforms baseline approaches across multiple metrics—including MAE, MAPE, and MSE—exhibits negligible mean bias, and shows strong generalization and stability, as confirmed by k-fold cross-validation. It is thus well-suited for applications such as route planning and accessibility analysis in low-traffic scenarios.
📝 Abstract
Accurate roadway travel-time prediction is foundational to transportation systems analysis, yet widespread reliance on either data-intensive congestion models or overly na\"ive heuristics limits scalability and practical adoption in engineering workflows. This paper develops a lightweight estimator for minimally-congested car travel times that integrates open road-network data, speed constraints, and sparse control/turn features within a random forest framework to correct bias from shortest-path traversal-time baselines. Using an urban testbed, the pipeline: (i) constructs drivable networks from volunteered geographic data; (ii) solves Dijkstra routes minimizing edge traversal time; (iii) derives sparse operational features (signals, stops, crossings, yield, roundabouts; left/right/slight/U-turn counts); and (iv) trains a regression ensemble on limited high-quality reference times to generalize predictions beyond the training set. Out-of-sample evaluation demonstrates marked improvements over traversal-time baselines across mean absolute error, mean absolute percentage error, mean squared error, relative bias, and explained variance, with no significant mean bias under minimally congested conditions and consistent k-fold stability indicating negligible overfitting. The resulting approach offers a practical middle ground for transportation engineering: it preserves point-to-point fidelity at metropolitan scale, reduces resource requirements, and supplies defensible performance estimates where congestion feeds are inaccessible or cost-prohibitive, supporting planning, accessibility, and network performance applications under low-traffic operating regimes.