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Urban traffic flow is governed by the complex, nonlinear interaction between land use configuration and spatiotemporally heterogeneous mobility demand. Conventional global regression and time-series models cannot simultaneously capture…
Accurate demand forecasting is critical for enhancing the efficiency and responsiveness of food delivery platforms, where spatial heterogeneity and temporal fluctuations in order volumes directly influence operational decisions. This paper…
Environmental data may be "large" due to number of records, number of covariates, or both. Random forests has a reputation for good predictive performance when using many covariates with nonlinear relationships, whereas spatial regression,…
Shared-use autonomous mobility services (SAMS) present new opportunities for improving accessible and demand-responsive mobility. A fundamental challenge that SAMS face is appropriate positioning of idle fleet vehicles to meet future demand…
We study a spatiotemporal service matching problem in which demand, heterogeneous in location and time sensitivity/preference, is to be assigned to service stations. The planner seeks to maximize social welfare, defined as total service…
Understanding the variability of people's travel patterns is key to transport planning and policy-making. However, to what extent daily transit use displays geographic and temporal variabilities, and what are the contributing factors have…
This paper focuses on the problem of predicting the future position of a target road user given its current state, consisting of position and velocity. A weighted average approach is adopted, where the weights are determined from data…
For traffic prediction in transportation services such as car-sharing and ride-hailing, mid-term road traffic prediction (within a few hours) is considered essential. However, the existing road-level traffic prediction has mainly studied…
Determining spatial distributions of species and communities are key objectives of ecology and conservation. Joint species distribution models use multi-species detection-nondetection data to estimate species and community distributions.…
Bike-sharing systems are a rapidly developing mode of transportation and provide an efficient alternative to passive, motorized personal mobility. The asymmetric nature of bike demand causes the need for rebalancing bike stations, which is…
Spatial data is ubiquitous. Massive amounts of data are generated every day from billions of GPS-enabled devices such as cell phones, cars, sensors, and various consumer-based applications such as Uber, Tinder, location-tagged posts in…
Area-level models for small area estimation typically rely on areal random effects to shrink design-based direct estimates towards a model-based predictor. Incorporating the spatial dependence of the random effects into these models can…
Next generation mobile networks will rely ever more heavily on resource sharing. In this article we study the sharing of radio access network and spectrum among mobile operators. We assess the impact of sharing these two types of resources…
Balancing passenger demand and vehicle availability is crucial for ensuring the sustainability and effectiveness of urban transportation systems. To address this challenge, we propose a novel hierarchical strategy for the efficient…
This paper models the availability of bikes at San Francisco Bay Area Bike Share stations using machine learning algorithms. Random Forest (RF) and Least-Squares Boosting (LSBoost) were used as univariate regression algorithms, and Partial…
As the number of Persons with Disabilities (PWD), particularly those with one or more physical impairments, increases, there is an increasing demand for assistive robotic technologies that can support independent mobility in the built…
Ride-hailing is rapidly changing urban and personal transportation. Ride sharing or pooling is important to mitigate negative externalities of ride-hailing such as increased congestion and environmental impacts. However, there lacks…
Current navigation systems conflate time-to-drive with the true time-to-arrive by ignoring parking search duration and the final walking leg. Such underestimation can significantly affect user experience, mode choice, congestion, and…
Understanding individual mobility behavior is critical for modeling urban transportation. It provides deeper insights on the generative mechanisms of human movements. Emerging data sources such as mobile phone call detail records, social…
Dynamic ride-sharing services, including ride-pooling offered by ride-hailing platforms and demand-responsive buses, have become an essential part of urban mobility systems. These services cater to personalized and on-demand mobility…