Designing an On-Demand Dynamic Crowdshipping Model and Evaluating its Ability to Serve Local Retail Delivery in New York City

Nowadays city mobility is challenging, mainly in populated metropolitan areas. Growing commute demands, increase in the number of for-hire vehicles, enormous escalation in several intra-city deliveries and limited infrastructure (road capacities), all contribute to mobility challenges. These challenges typically have significant impacts on residents’ quality-of-life particularly from an economic and environmental perspective. Decision-makers have to optimize transportation resources to minimize the system externalities (especially in large-scale metropolitan areas). This thesis focus on the intra-city mobility problems experienced by travelers (in the form of congestion and imbalance taxi resources) and businesses (in the form of last-mile delivery), while taking into consideration a measurement of potential adoption by citizens (in the form of a survey). To find solutions for this mobility problem this dissertation proposes three distinct and complementary methodological studies. First, taxi demand is predicted by employing a deep learning approach that leverages Long Short-Term Memory (LSTM) neural networks, trained over publicly available New York City taxi trip data. Taxi pickup data are binned based on geospatial and temporal informational tags, which are then clustered using a technique inspired by Principal Component Analysis. The spatiotemporal distribution of the taxi pickup demand is studied within short-term periods (for the next hour) as well as long-term periods (for the next 48 hours) within each data cluster. The performance and robustness of the LSTM model are evaluated through a comparison with Adaptive Boosting Regression and Decision Tree Regression models fitted to the same datasets. On the next study, an On-Demand Dynamic Crowdshipping system is designed to utilize excess transport capacity to serve parcel delivery tasks and passengers collectively. This method is general and could be expanded and used for all types of public transportation modes depending upon the availability of data. This system is evaluated for the case study of New York City and to assess the impacts of the crowdshipping system (by using taxis as carriers) on trip cost, vehicle miles traveled, and people travel behavior. Finally, a Stated Preference (SP) survey is presented, designed to collect information about people’s willingness to participate in a crowdshipping system. The survey is analyzed to determine the essential attributes and evaluate the likelihood of individuals participating in the service either as requesters or as carriers. The survey collects information on the preferences and important attributes of New York citizens, describing what segments of the population are willing to participate in a crowdshipping system. While the transportation problems are complex and approximations had to be done within the studies to achieve progress, this dissertation provides a comprehensive way to model and understand the potential impact of efficient utilization of existing resources on transportation systems. Generally, this study offer insights to decisions makers and academics about potential areas of opportunity and methodologies to optimize the transportation system of densely populated areas. This dissertation offers methods that can optimize taxi distribution based on the demand, optimize costs for retail delivery, while providing additional income for individuals. It also provides valuable insights for decision makers in terms of collecting population opinion about the service and analyzing the likelihood of participating in the service. The analysis provides an initial foundation for future modeling and assessment of crowdshipping

A multi-functional simulation platform for on-demand ride service operations

On-demand ride services or ride-sourcing services have been experiencing fast development in the past decade. Various mathematical models and optimization algorithms have been developed to help ride-sourcing platforms design operational strategies with higher efficiency. However, due to cost and reliability issues (implementing an immature algorithm for real operations may result in system turbulence), it is commonly infeasible to validate these models and train/test these optimization algorithms within real-world ride sourcing platforms. Acting as a useful test bed, a simulation platform for ride-sourcing systems will be very important to conduct algorithm training/testing or model validation through trails and errors. While previous studies have established a variety of simulators for their own tasks, it lacks a fair and public platform for comparing the models or algorithms proposed by different researchers. In addition, the existing simulators still face many challenges, ranging from their closeness to real environments of ride-sourcing systems, to the completeness of different tasks they can implement. To address the challenges, we propose a novel multi-functional and open-sourced simulation platform for ride-sourcing systems, which can simulate the behaviors and movements of various agents on a real transportation network. It provides a few accessible portals for users to train and test various optimization algorithms, especially reinforcement learning algorithms, for a variety of tasks, including on-demand matching, idle vehicle repositioning, and dynamic pricing. In addition, it can be used to test how well the theoretical models approximate the simulated outcomes. Evaluated on real-world data based experiments, the simulator is demonstrated to be an efficient and effective test bed for various tasks related to on-demand ride service operations

Investigation, Modeling, and Analysis of Integrated Metroplex Arrival and Departure Coordination Concepts

This work involves the development of a concept that enhances integrated metroplex arrival and departure coordination, determines the temporal (the use of time separation for aircraft sharing the same airspace resources) and spatial (the use of different routes or vertical profiles for aircraft streams at any given time) impact of metroplex traffic coordination within the National Airspace System (NAS), and quantifies the benefits of the most desirable metroplex traffic coordination concept. Researching and developing metroplex concepts is addressed in this work that broadly applies across the range of airspace and airport demand characteristics envisioned for NextGen metroplex operations. The objective of this work is to investigate, formulate, develop models, and analyze an operational concept that mitigates issues specific to the metroplex or that takes advantage of unique characteristics of metroplex airports to improve efficiencies. The concept is an innovative approach allowing the NAS to mitigate metroplex interdependencies between airports, optimize metroplex arrival and departure coordination among airports, maximize metroplex airport throughput, minimize delay due to airport runway configuration changes, increase resiliency to disruptions, and increase the tolerance of the system to degrade gracefully under adverse conditions such as weather, traffic management initiatives, and delays in general

Data-Driven Dynamic Robust Resource Allocation: Application to Efficient Transportation

The transformation to smarter cities brings an array of emerging urbanization challenges. With the development of technologies such as sensor networks, storage devices, and cloud computing, we are able to collect, store, and analyze a large amount of data in real time. Modern cities have brought to life unprecedented opportunities and challenges for allocating limited resources in a data-driven way. Intelligent transportation system is one emerging research area, in which sensing data provides us opportunities for understanding spatial-temporal patterns of demand human and mobility. However, greedy or matching algorithms that only deal with known requests are far from efficient in the long run without considering demand information predicted based on data. In this dissertation, we develop a data-driven robust resource allocation framework to consider spatial-temporally correlated demand and demand uncertainties, motivated by the problem of efficient dispatching of taxi or autonomous vehicles. We first present a receding horizon control (RHC) framework to dispatch taxis towards predicted demand; this framework incorporates both information from historical record data and real-time GPS location and occupancy status data. It also allows us to allocate resource from a globally optimal perspective in a longer time period, besides the local level greedy or matching algorithm for assigning a passenger pick-up location of each vacant vehicle. The objectives include reducing both current and anticipated future total idle driving distance and matching spatial-temporal ratio between demand and supply for service quality. We then present a robust optimization method to consider spatial-temporally correlated demand model uncertainties that can be expressed in closed convex sets. Uncertainty sets of demand vectors are constructed from data based on theories in hypothesis testing, and the sets provide a desired probabilistic guarantee level for the performance of dispatch solutions. To minimize the average resource allocation cost under demand uncertainties, we develop a general data-driven dynamic distributionally robust resource allocation model. An efficient algorithm for building demand uncertainty sets that compatible with various demand prediction methods is developed. We prove equivalent computationally tractable forms of the robust and distributionally robust resource allocation problems using strong duality. The resource allocation problem aims to balance the demand-supply ratio at different nodes of the network with minimum balancing and re-balancing cost, with decision variables on the denominator that has not been covered by previous work. Trace-driven analysis with real taxi operational record data of San Francisco shows that the RHC framework reduces the average total idle distance of taxis by 52%, and evaluations with over 100GB of New York City taxi trip data show that robust and distributionally robust dispatch methods reduce the average total idle distance by 10% more compared with non-robust solutions. Besides increasing the service efficiency by reducing total idle driving distance, the resource allocation methods in this dissertation also reduce the demand-supply ratio mismatch error across the city

Aeronautical Engineering: A special bibliography with indexes, supplement 72, July 1976

This bibliography lists 184 reports, articles, and other documents introduced into the NASA scientific and technical information system in June 1976