Next Location Prediction Model: A Geohashed Based Recurrent Neural Network

This work investigates the significance of choosing appropriate recurrent neural networks (RNNs) architecture for a spatiotemporal next location prediction framework. Dockless shared micro-mobility sharing programs provide spatial trajectory data that entails essential information for city planners and developers. The study compares (i) the variable-sized geohash tessellation and (ii) two common RNN architectures: Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), using bike/scooter location data for Washington DC, USA. LSTM and GRU networks are used for modeling and incorporating information from spatial neighbors into the model. The study suggests that the LSTM model yields slightly better performance than the GRU model based on the same tessellation. However, geohash size might play a significant role in model performance. The study highlights the need to explore hyperparameter tuning, multiple spatial partitioning techniques especially with the Google S2 library, and more trip data for improving the prediction performance in neural network models

Three Essays on Shared Micromobility

Shared micromobility defines as the shared use of light and low-speed vehicles such as bike and scooter in which users have short-term access on an as-needed basis. As shared micromobility, as one of the most viable and sustainable modes of transportation, has emerged in the U.S. over the last decade., understanding different aspects of these modes of transportation help decision-makers and stakeholders to have better insights into the problems related to these transportation options. Designing efficient and effective shared micromobility programs improves overall system performance, enhances accessibility, and is essential to increase ridership and benefit commuters. This dissertation aims to address three vital aspects of emerging shared micromobility transportation options with three essays that each contribute to the practice and literature of sustainable transportation. Chapter one of this dissertation investigates public opinion towards dockless bikes sharing using a mix of statistical and natural language processing methods. This study finds the underlying topics and the corresponding polarity in public discussion by analyzing tweets to give better insight into the emerging phenomenon across the U.S. Chapter two of this dissertation proposes a new framework for the micromobility network to improve accessibility and reduce operator costs. The framework focuses on highly centralized clubs (known as k-club) as virtual docking hubs. The study suggests an integer programming model and a heuristic approach as well as a cost-benefit analysis of the proposed model. Chapter three of this dissertation address the risk perception of bicycle and scooter riders’ risky behaviors. This study investigates twenty dangerous maneuvers and their corresponding frequency and severity from U.S. resident’s perspective. The resultant risk matrix and regression model provides a clear picture of the public risk perception associated with these two micromobility options. Overall, the research outcomes will provide decision-makers and stakeholders with scientific information, practical implications, and necessary tools that will enable them to offer better and sustainable micromobility services to their residents

Future of Rural Transit

This paper provides a contemplative description of the future of rural public transportation. It considers emerging technologies along with their long-term implications and corresponding impacts on rural communities. The authors used their collective knowledge to identify key drivers of change in rural areas. As a result, the authors expect the future definition of rural areas to change and a new geographical classification to emerge. This classification is a continuum of population density gradient from highly populated urban areas to sparsely populated areas. The paper also suggests that automated vehicles and hologram telecommuting could dominate the U.S. transportation industry, even in rural settings

Maximum Closeness Centrality k-Clubs: A Study of Dock-Less Bike Sharing

In this work, we investigate a new paradigm for dock-less bike sharing. Recently, it has become essential to accommodate connected and free-floating bicycles in modern bike-sharing operations. This change comes with an increase in the coordination cost, as bicycles are no longer checked in and out from bike-sharing stations that are fully equipped to handle the volume of requests; instead, bicycles can be checked in and out from virtually anywhere. In this paper, we propose a new framework for combining traditional bike stations with locations that can serve as free-floating bike-sharing stations. The framework we propose here focuses on identifying highly centralized k-clubs (i.e., connected subgraphs of restricted diameter). The restricted diameter reduces coordination costs as dock-less bicycles can only be found in specific locations. In addition, we use closeness centrality as this metric allows for quick access to dock-less bike sharing while, at the same time, optimizing the reach of service to bikers/customers. For the proposed problem, we first derive its computational complexity and show that it is NP-hard (by reduction from the 3-SATISFIABILITY problem), and then provide an integer programming formulation. Due to its computational complexity, the problem cannot be solved exactly in a large-scale setting, as is such of an urban area. Hence, we provide a greedy heuristic approach that is shown to run in reasonable computational time. We also provide the presentation and analysis of a case study in two cities of the state of North Dakota: Casselton and Fargo. Our work concludes with the cost-benefit analysis of both models (docked vs. dockless) to suggest the potential advantages of the proposed model

Application of a Multi-Agent System with the Large-Scale Agent-Based Model for Freight Demand Modeling [Research Brief]

MPC-458In this study, researchers used the agent-based simulation (ABS) for modeling agricultural transportation demand. With the simulation model, geographic information systems (GIS) was utilized to collect and analyze remote sensing of agricultural crops. The researchers reviewed agent-based modeling in freight and public transportation planning to fill the gap between traditional modeling efforts and emerging needs of adopting behavioral modeling for an agricultural transportation model. The study then investigated adoption of an agent-based model for a large-scale travel demand model. The objective of the proposed model was to provide a platform to analyze grain transportation movement at the micro-level, since most industry reports lack such micro-level freight analysis while supporting regional and statewide planning