With the advancement of AI-based traffic prediction and drone technologies, redesigning fixed sensor networks and integrating drone fleets as dynamic supplements presents a cost-effective approach to enhancing traffic management. Traditional sensor networks are typically coverage-driven, aiming to maximize spatial reach. However, recent AI-driven studies have shown that such layouts result in redundant sensors collecting highly correlated data. In practice, reliable predictions can be achieved with fewer sensors, provided they are strategically placed. Motivated by this insight, this study proposes a prediction-driven design for a hybrid monitoring system and explores its relationship with prediction performance.

A two-stage optimization framework is proposed to balance system cost and predictive accuracy. In Stage 1, a genetic algorithm (GA) is used to optimize the infrastructure-level design, minimizing the number of installed fixed sensors, the peak number of simultaneously active drones, and the root mean square error (RMSE) of a pretrained random forest (RF) prediction model. The model is evaluated on masked datasets representing the sensor network’s observable data. In Stage 2, a simulated annealing (SA) algorithm solves a drone routing problem to fulfill the drone monitoring tasks generated in Stage 1 while minimizing operational and procurement costs.

The framework is applied to the pNEUMA dataset, a high-resolution drone-based urban traffic dataset. Results demonstrate that the hybrid system consistently outperforms fixed-sensor-only configurations across all budget levels. The most significant cost savings (up to 40\%) are achieved under moderate budget scenarios. Fixed sensors are shown to be essential for long-term coverage in critical zones with high traffic volume or strong temporal dynamics, while drones effectively supplement under-monitored peripheral regions and provide adaptive temporal monitoring. As the drone fleet size increases, both sensor types are more uniformly distributed, supporting a system design that balances spatial coverage with predictive performance.

Public transport disruptions can significantly affect passengers' travel behavior, yet little is known about how route choice preferences evolve after a disruption ends. This study investigates how passengers' route choices change in response to a planned disruption, using Automated Fare Collection (AFC) data from the Washington DC metro system. Unlike previous research that often relies on stated preference surveys or focuses only on the disruption period, this study analyzes several months of pre- and post-disruption behavior to assess whether changes in preferences persist over time.

Descriptive analysis reveals that although travel times and wait times remained relatively stable, passengers shifted from preferring direct routes to favoring routes with more transfers but shorter in-vehicle times after the disruption. Discrete choice models, including Multinomial Logit and Mixed Logit models, were estimated to explore these shifts, but the results showed unexpected coefficient signs, likely due to strong multicollinearity between travel time and transfers, and the presence of dominated alternatives. While the Mixed Logit model improved the model fit slightly, practical interpretability remained limited.

The findings suggest that disruptions can lead to lasting behavioral changes, with passengers reassessing their travel options rather than returning to previous habits. Although AFC data is valuable for detecting such shifts, it alone is insufficient to fully capture the factors driving route choice behavior, highlighting the need for complementary perception-based data in future research. From a policy perspective, understanding these behavioral adaptations can help transit agencies design better service recovery strategies that sustain ridership after disruptions, supporting broader goals of promoting sustainable urban mobility.

Disruptions in urban public transport networks significantly impact urban mobility, causing inconveniences for passengers, economic losses, and vulnerabilities in transit systems. Major disruptions may lead some passengers to permanently switch to alternative modes like cars, even after the disruptions resolve, potentially worsening congestion and environmental issues. While the immediate impact of disruptions on behavior has been extensively studied, there is limited research on the long-term effects, often restricted to qualitative methods. This study aims to address this gap by proposing a framework to investigate the prolonged effects of public transport disruptions on passenger travel behavior using smart card data. A mixture latent Markov model is used to track passenger behavior from the pre-disruption to the post-disruption period. This framework identifies travel patterns and tracks how passengers transition between these patterns over time, thus inferring the impact of disruptions on behavior. Each passenger is assigned a mobility style that reflects their general travel attitude, such as those who do not change their behavior. The results from our case study reveal that the impact of the disruption was not as substantial as anticipated, with a high proportion of passengers maintaining their behavior.