Negative externalities refer to costs arising from transportation activities that are not borne by service providers or consumers, often leading to their neglect in freight transportation planning. This study proposes a novel framework for optimising water‐road transportation route selection by incorporating two key negative externalities: fuel consumption and traffic risk. Traffic risk is assessed using a safety performance function, while fuel consumption is estimated based on the Handbook of Emission Factors for Road Transport. The proposed framework is applied to California's road network and port system, where the optimal operation area for each major port is determined and compared across different optimisation objectives: trip distance, fuel consumption and traffic risk. Results indicate that the optimal operation area varies significantly depending on the relative weight assigned to each objective. The findings demonstrate that optimising routes beyond just minimising distance can reduce fuel consumption and traffic risk, highlighting the substantial differences in optimal operational areas under different criteria.

Road hazardous material transportation plays a critical role in road traffic management. Due to the dangerous nature of the cargo, hazardous material transportation trucks (HMTTs) have different route selection and driving characteristics compared to traditional freight trucks. These differences lead to unique travel and emission patterns, which in turn affect traffic management strategies and emission control measures. However, existing research predominantly focuses on safety aspects related to individual vehicle behavior, with limited exploration of the broader travel and emission characteristics of HMTTs. To bridge this gap, this study develops a comprehensive framework for analyzing the travel patterns and emissions of HMTTs. The methodology begins by applying a Gaussian mixture distribution model to identify vehicle stop points, eliminating biases associated with subjective settings. Origin–destination (OD) pairs are then determined through stop time clustering, followed by the extraction of travel characteristics using non-negative matrix factorization. Emissions are subsequently calculated based on the identified trip data. The relationship between emissions and land use characteristics is further analyzed using geographically weighted regression (GWR). Crucially, this study leverages data from the BeiDou Satellite Navigation System, focusing on HMTTs operating within Shanghai. The processed data reveal three distinct travel modes of HMTTs, categorized by spatiotemporal patterns: Daytime—Surrounding cities, Early morning—In-city, and Midnight—Scattered. Moreover, unlike other road vehicles, HMTT emissions are heavily influenced by industrial and company-related points of interest (POIs). These findings highlight the significant role of BeiDou Satellite Navigation System data in optimizing HMTT management strategies to reduce emissions and improve overall safety.

Modern navigation systems prioritize fuel efficiency and time savings, often overlooking the critical aspect of road safety. This study addresses this gap by developing an advanced safe route guidance approach for Electronic Route Guidance Systems that incorporates personalized safety metrics based on individual driving behaviors and road conditions. Using the Safety Performance Function to evaluate the overall risk of road segments, and a copula model to capture the correlation between driving behaviors and road risk, this research uses conditional probability theory to quantify the safety levels of road segments for drivers with different driving behaviors to tailor safer route recommendations. The proposed approach is validated using the crash data, road geometric data, and driving behavior data collected from Los Angeles, demonstrating its capability to customize navigation based on individual driving behavior and significantly enhance route safety. The findings suggest that the safest route may vary depending on driving behavior. This research not only incorporates individualized traffic safety in navigation but also offers a scalable framework for future navigation systems to incorporate safety as a fundamental component.