Search for: All records

Ride-sourcing services play an increasingly important role in meeting mobility needs in many metropolitan areas. Yet, aside from delivering passengers from their origins to destinations, ride-sourcing vehicles generate a significant number of vacant trips from the end of one customer delivery trip to the start of the next. These vacant trips create additional traffic demand and may worsen traffic conditions in urban networks. Capturing the congestion effect of these vacant trips poses a great challenge to the modeling practice of transportation planning agencies. With ride-sourcing services, vehicular trips are the outcome of the interactions between service providers and passengers, a missing ingredient in the current traffic assignment methodology. In this paper, we enhance the methodology by explicitly modeling those vacant trips, which include cruising for customers and deadheading for picking up them. Because of the similarity between taxi and ride-sourcing services, we first extend previous taxi network models to construct a base model, which assumes intranode matching between customers and idle ride-sourcing vehicles and thus, only considers cruising vacant trips. Considering spatial matching among multiple zones commonly practiced by ride-sourcing platforms, we further enhance the base model by encapsulating internode matching and considering both the cruising and deadheading vacant trips. A large set of empirical data from Didi Chuxing is applied to validate the proposed enhancement for internode matching. The extended model describes the equilibrium state that results from the interactions between background regular traffic and occupied, idle, and deadheading ride-sourcing vehicles. A solution algorithm is further proposed to solve the enhanced model effectively. Numerical examples are presented to demonstrate the model and solution algorithm. Although this study focuses on ride-sourcing services, the proposed modeling framework can be adapted to model other types of shared use mobility services. 

The forecasting of significant societal events such as civil unrest and economic crisis is an interesting and challenging problem which requires both timeliness, precision, and comprehensiveness. Significant societal events are influenced and indicated jointly by multiple aspects of a society, including its economics, politics, and culture. Traditional forecasting methods based on a single data source find it hard to cover all these aspects comprehensively, thus limiting model performance. Multi-source event forecasting has proven promising but still suffers from several challenges, including (1) geographical hierarchies in multi-source data features, (2) hierarchical missing values, (3) characterization of structured feature sparsity, and (4) difficulty in model’s online update with incomplete multiple sources. This article proposes a novel feature learning model that concurrently addresses all the above challenges. Specifically, given multi-source data from different geographical levels, we design a new forecasting model by characterizing the lower-level features’ dependence on higher-level features. To handle the correlations amidst structured feature sets and deal with missing values among the coupled features, we propose a novel feature learning model based on an N th-order strong hierarchy and fused-overlapping group Lasso. An efficient algorithm is developed to optimize model parameters and ensure global optima. More importantly, to enable the model update in real time, the online learning algorithm is formulated and active set techniques are leveraged to resolve the crucial challenge when new patterns of missing features appear in real time. Extensive experiments on 10 datasets in different domains demonstrate the effectiveness and efficiency of the proposed models.