Travelling is a critical component of daily life. With new technology, personalized travel route recommendations are possible and have become a new research area. A personalized travel route recommendation refers to plan an optimal travel route between two geographical locations, based on the road networks and users’ travel preferences. In this paper, we define users’ travel behaviours from their historical Global Positioning System (GPS) trajectories and propose two personalized travel route recommendation methods – collaborative travel route recommendation (CTRR) and an extended version of CTRR (CTRR+). Both methods consider users’ personal travel preferences based on their historical GPS trajectories. In this paper, we first estimate users’ travel behaviour frequencies by using collaborative filtering technique. A route with the maximum probability of a user’s travel behaviour is then generated based on the naïve Bayes model. The CTRR+ method improves the performances of CTRR by taking into account cold start users and integrating distance with the user travel behaviour probability. This paper also conducts some case studies based on a real GPS trajectory data set from Beijing, China. The experimental results show that the proposed CTRR and CTRR+ methods achieve better results for travel route recommendations compared with the shortest distance path method.

Preventive health care programs can save lives and contribute to a better quality of life by diagnosing serious medical conditions early. The Preventive Health Care Facility Location (PHCFL) problem is to identify optimal locations for preventive health care facilities so as to maximize participation. When identifying locations for preventive health care facilities, we need to consider the characteristics of the preventive health care services. First, people should have more flexibility to select service locations. Second, each preventive health care facility needs to have a minimum number of clients in order to retain accreditation.

With the advent of GPS tracking technology, how to make use of taxi trajectories to efficiently and effectively reduce taxis cruising distance is an active and challenging research topic. In this paper, we propose a profitable taxi route recommendation method called adaptive shortest expected cruising route (ASER). In ASER, a probabilistic network model is developed to predict pick-up probability and capacity of each location by using Kalman filtering method. To recommend profitable driving routes to taxi drivers, ASER takes the load balance between passengers and taxis into consideration and the shortest expected cruising distance is introduced to formulate potential cruising distance of taxis. Moreover, MapReduce and a novel data structure kdS-tree are applied to improve recommendation efficiency. ASER is evaluated on two real trajectory datasets from San Francisco, CA, USA, and Wuhan, China. The experimental results validate that ASER significantly outperforms the existing methods by reducing the taxi cruising distance 11% and 39%.

As catastrophic phenomena, landslides often cause large-scale socio-economic destruction including loss of life, economic collapse, and human injury. In addition, landslides can impair the functioning of critical infrastructure and destroy cultural heritage and ecological systems. In order to build a more landslide resistant and resilient society, an original GIS-based decision support system is put forth in order to help emergency managers better prepare for and respond to landslide disasters. The GIS-based landslide monitoring and management system includes a Central Repository System (CRS), Disaster Data Processing Modules (DDPM), a Command and Control System (CCS) and a Portal Management System (PMS). This architecture provides valuable insights into landslide early warning, landslide risk and vulnerability analyses, and critical infrastructure damage assessments. Finally, internet-based communications are used to support landslide disaster modelling, monitoring and management.

Fleet sizing is critical for shared autonomous vehicle(SAV) fleet management to reduce maintenance costs and traffic congestions. As the passenger demands are dynamic, how to make the demand-aware dynamic ridesharing and calculate the fleet size is an important and challenging problem. In this paper, we propose a minimum fleet sizing method called Fleet Sizing for demand-aware Dynamic Ridesharing (FSDR) to accurately determine the fleet size for ridesharing enabled SAV system. Specifically, the travel demands are first predicted by an ensemble method that takes account of temporal correlations between regions. Then a concept called demand utility is proposed to measure the travel demands when planning vehicle paths, and the ride-matching dispatches vehicles to high travel demand regions by maximizing the demand utility along the vehicle path. Based on the ride-matching result, the minimum fleet size is calculated based on a trip graph by the Hopcroft-Karp algorithm. FSDR is evaluated on two real GPS taxi trajectory datasets from Wuhan, China, and San Francisco, USA. The result validates that the proposed demand-aware ridesharing in FSDR can significantly reduce the fleet size compared to the existing ride-matching methods. Moreover, the result shows that by allowing ridesharing, the vehicle fleet size can be reduced by 30%.