Travel time reliability (TTR) is widely used to evaluate transportation system performance. Adverse weather condition is an important factor for affecting TTR, which can cause traffic congestions and crashes. Considering the traffic characteristics under different traffic conditions, it is necessary to explore the impact of adverse weather on TTR under different conditions. This study conducted an empirical travel time analysis using traffic data and weather data collected on Yanan corridor in Shanghai. The travel time distributions were analysed under different roadway types, weather, and time of day. Four typical scenarios (i.e., peak hours and off-peak hours on elevated expressway, peak hours and off-peak hours on arterial road) were considered in the TTR analysis. Four measures were calculated to evaluate the impact of adverse weather on TTR. The results indicated that the lognormal distribution is preferred for describing the travel time data. Compared with off-peak hours, the impact of adverse weather is more significant for peak hours. The travel time variability, buffer time index, misery index, and frequency of congestion increased by an average of 29%, 19%, 22%, and 63%, respectively, under the adverse weather condition. The findings in this study are useful for transportation management agencies to design traffic control strategies when adverse weather occurs.

Resilience against extreme rainfall and its induced flooding is critical for a truck freight system during extreme events and post-event recovery. This study presents a two-step modeling framework that integrates a flood simulation model and a freight demand model to quantify the resilience of the truck freight system against extreme rainfall events. In the initial step, using rainfall data from meteorological stations, the catchment-based macro-scale floodplain (CaMa-Flood) model was introduced to simulate the rainfall event and its impacts on each road segment’s capacity within the study region. Then, a regional truck freight demand model was built using vehicle trajectory data from heavy-duty trucks operating during the study period to simulate the travel time changes for each affected road segment as a metric to analyze their importance to both functional and topological resilience of the truck freight system. These road segments were ranked based on the travel time increases, with the segment showing the greatest increase ranked as the most critical. To validate the proposed method, an extreme rainfall event in Beijing, Tianjin, and Hebei in July 2023 was modeled. The proposed method can be used to identify key infrastructure improvements to minimize disruptions to the truck freight system, providing decision support for climate-resilient transportation planning essential for achieving UN Sustainable Development Goals (SDG 9 on resilient infrastructure and SDG 13 on climate action).

Rainfall introduces operational uncertainty for battery electric bus (BEB) fleets constrained by fixed schedules and limited charging windows. This study develops a vehicle-specific deviation framework to quantify how rainfall alters operating performance relative to clear-day baselines and links these changes to charging behavior. Using high-resolution trajectory and battery data from 105 BEBs in Shanghai, covering more than 13,600 trips and 2,100 charging events, five deviation indicators capture shifts in speed, stability, energy intensity, idling, and braking. A Gaussian Mixture Model identifies four distinct operating response types, revealing substantial heterogeneity across vehicles and routes. Stop-and-go and smooth-down responses are concentrated on transport corridors and associated with lower speeds, higher braking intensity, and reduced stability. Charging adjustments mainly involve earlier and more frequent sessions within non-operating windows rather than increases in daily energy intake. The findings demonstrate how rainfall reshapes operational–energy coordination and provide a transferable framework for weather-aware BEB management.