travel time estimation

This paper addresses the ill-posedness arising from sparse observational data in simulation-driven origin–destination (OD) demand calibration. We propose a prior-free joint calibration method that directly incorporates sparse link count observations into the simulation-based optimization objective as a data-driven ℓ₂ regularization term, simultaneously constraining both link flow distribution and path travel times. Our approach establishes a tightly coupled simulation–optimization framework integrating dynamic traffic assignment (DTA) with gradient approximation algorithms to enable end-to-end calibration. Evaluated on multiple congestion scenarios within the Seattle highway network, the method achieves a 37% improvement in OD demand recovery accuracy and reduces link flow fitting error by 52%, significantly outperforming conventional prior-dependent OD calibration approaches.

This study addresses the insufficient spatial resolution of conventional four-step travel demand models for small geographic units (e.g., census tracts), which impedes evidence-based, localized policy interventions. To overcome this limitation, we propose a high-resolution framework for estimating travel behavior at fine-grained spatial scales. Methodologically, the framework integrates publicly available microdata (e.g., ACS/PUMS) with synthetic population generation and machine learning techniques to construct an end-to-end system covering trip generation, distribution, mode choice, and route assignment. Its key contributions include breaking traditional spatial scalability constraints, enabling interpretable, equity-aware modeling—particularly for vulnerable populations—and supporting context-specific applications such as micro-distribution center siting, curbside management, and inclusive transportation design. Empirical evaluation on a commuter dataset demonstrates statistically significant improvements in prediction accuracy over classical four-step and gravity-model baselines.

This study addresses the high uncertainty in travel times following subway disruptions, caused by irregular train operations during service recovery. The authors propose a Bayesian spatiotemporal modeling framework that decomposes travel time into a baseline component and a delay component. Innovatively, the model jointly captures inter-train dependencies through a moving average error structure, accounts for headway imbalance, and characterizes the asymmetric heavy-tailed nature of travel times during recovery periods using skew-normal and skew-t distributions. Evaluated on high-resolution track occupancy data from the Montreal metro system, the approach outperforms existing baselines in both point prediction accuracy and uncertainty quantification, with the skew-t variant demonstrating particularly robust performance for long-distance trips.

Arc travel time estimation and path choice model parameter estimation in road transportation networks are inherently interdependent, yet traditionally addressed separately, leading to biased estimates due to ignored coupling. Method: This paper proposes the first joint maximum likelihood estimation framework, built upon a differentiable path choice model that integrates random utility theory with numerical optimization. It supports unified modeling of multi-granularity, noisy, and partially observed path data—including real-world GPS trajectories (e.g., NYC taxi data). Contribution/Results: Our approach enables end-to-end joint estimation of travel times and path choice parameters—the first such method—effectively mitigating bias inherent in sequential estimation. Experiments demonstrate significant improvements in path choice parameter accuracy and superior arc travel time estimation performance compared to link-only baselines. This framework provides a more reliable foundation for both strategic and tactical transportation network planning.

Existing approaches to modeling path travel time struggle to balance accuracy and computational efficiency: models relying on independence assumptions suffer from poor calibration, while complex simulation-based methods incur prohibitive computational costs. This work proposes a conjugate Bayesian dynamic Gamma model that captures inter-segment dependencies through a shared latent environmental process, preserving conditional independence for tractable inference. By leveraging moment-matching approximation, the method yields a closed-form F-distribution for path travel time with O(1) computational complexity—the first such result to our knowledge. Evaluated on an 8.26-mile segment of Chicago’s I-55 freeway, the approach achieves a 95.4% empirical coverage rate within its 90% prediction intervals, substantially outperforming independent models (34–37%) at comparable computational cost, thereby overcoming the longstanding trade-off between precision and efficiency.

This study addresses the challenge of city-scale driving travel time prediction for researchers with limited resources, particularly in the absence of expensive commercial APIs or large-scale proprietary datasets. The authors propose a lightweight, open-source solution with low computational overhead that leverages only OpenStreetMap-derived features—such as route length, number of turns, and traffic signal counts—to construct an interpretable random forest model. Notably, the approach achieves reasonable prediction accuracy without requiring real-time traffic data. Empirical evaluation in the Los Angeles metropolitan area demonstrates that the method significantly outperforms naive baseline estimates, offering an efficient, interpretable, and easily deployable tool for travel time prediction in resource-constrained settings.

Existing approaches struggle to generate individual daily activity schedules that are both realistic and temporally consistent with reported travel survey data. This work proposes an iterative optimization framework that integrates dynamic programming, travel time simulation, and activity location assignment algorithms to progressively refine synthetic activity schedules while preserving privacy. By iteratively adjusting activity timing and sequencing, the method aligns simulated travel times with empirical distributions derived from travel surveys. Experimental results demonstrate that the proposed approach reduces the discrepancy between simulated and observed travel times by 52.2% compared to initial schedules, substantially improving both temporal consistency and behavioral realism in the generated activity plans.

This study addresses the influence of weather and seasonal factors on network-wide Travel Time Index (TTI) by proposing a machine learning prediction framework that explicitly integrates meteorological and seasonal features. Leveraging over 50,000 TTI observations collected over six years in Washington, D.C., the authors systematically incorporate weather and seasonal variables into predictive models and comparatively evaluate the performance of Ridge Regression, Support Vector Machines, and other methods for both short-term and long-term forecasting. The work presents the first quantitative assessment of weather- and season-induced effects on TTI at the network scale, demonstrating that Ridge Regression consistently outperforms competing models across all prediction tasks, yielding significantly improved accuracy. These findings offer a robust methodological foundation for intelligent transportation management under varying environmental conditions.

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.