Design and Throughput Capacity Evaluation of Pickup and Dropoff (PUDO) Facilities
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Liu, Xinyu
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Abstract
Pickup and dropoff (PUDO) facilities have long been important to facilitate passenger transportation and goods delivery. Typical examples of passenger PUDO facilities are expected at inter-mobility stations or terminals where passengers switch between different transportation modes, outside the stadiums where large-scale sports events or concerts are hosted, and at schools where students are dropped off in the morning and picked up in the afternoon. Meanwhile, the last mile of the urban freight delivery system has gauged unprecedented and continuing attention since the booming of e-commerce and time-sensitive delivery services, leading to frequent pickups and dropoffs of goods close to their end consumers. The recent increase in the adoption of on-demand transportation provided by transportation network companies, and the anticipated continuation of this trend with the introduction of self-driving vehicles, imply that PUDO facilities will become more widespread and substantial. The current practice of mostly using curbsides for PUDO operations will not be sustainable as demand increases. This dissertation presents stochastic modeling and computational tools to evaluate the throughput capacity of PUDO facilities in various contexts. This dissertation also studies the optimal design decisions and characterizes near-optimal approximate policies to inform practical and implementable solutions in the real world.
Chapter 1 presents a modeling and computational framework to evaluate layout designs and operational policies for passenger PUDO facilities, with realistic representations of the conflicting movements among vehicles and the resulting trade-off between space utilization and vehicle throughput rate. The results show the throughput capacities of PUDO facilities exhibit decreasing returns to scale, which are not captured by existing analytical approaches such as linear curb length models and queueing approximations. Several stochastic models are proposed to compute optimal and near-optimal throughput capacities. Renewal-reward process calculations are presented for single-lane facilities often encountered at schools and taxi ranks. For more general facility layouts, we propose a continuous-time Markov decision process to optimize the operational policies and to evaluate the performance of approximate policies. A microscopic trajectory-based simulation is developed to validate the stochastic models. Findings and computational insights are presented, including comparisons of facility layouts and operational policies, as well as insights into the behavior of congested systems.
In Chapter 2, we study the throughput capacities of airport PUDO facilities. An airport serves as an interface between ground and air transportation, and therefore the efficient processing of ground transportation arrivals and departures is an important part of airport operations. At many airports, the current PUDO locations for taxis and other passenger cars are along the terminal curb or in existing parking facilities, and many of these PUDO facilities suffer from excessive congestion. The increased adoption of ride-hailing services has contributed to the growing use of on-demand ground transportation to and from airports, which aggravates congestion at terminal curbs. Since most airports are severely space-constrained, there is a need to consider PUDO facilities that are more efficient than terminal curbs, in terms of vehicle throughput per unit area. We consider the effect of the facility layout and operational rules on conflicts between the movements of different vehicles, the resulting delays in the movements of vehicles, as well as the spatial requirements of different layouts. We demonstrate the impact of mean service times, variability in service times and vehicle movement times, and operational rules on the relative throughput capacities of different facility layouts.
Chapter 3 discusses computational models to evaluate the throughput capacity of accessible PUDO facilities with a mix of wheelchair-accessible and regular spots, making provisions for passengers with reduced mobility. Improving transportation accessibility and usability for passengers with reduced mobility has long been a major objective of transportation service providers and facility designers. The design of accessible transportation facilities is guided and enforced by legislation and regulations, such as the 2010 ADA Standards. With the growing use of mobility services involving pickups and dropoffs of passengers, there is a pressing need for the design and operations of PUDO facilities that are both efficient and accessible to mobility-challenged passengers. We compare the throughput capacities of different facility layouts and operational policies, as a function of facility sizes. The major decision variables are (i) the number of regular and accessible spots in the facility; and (ii) the operational policies to control the use of both types of spots by vehicles with and without handicap registration. We propose a continuous-time Markov decision process and show the optimality of a family of threshold policies analytically, such that accessible spots can be assigned to vehicles with and without handicap registration as long as a sufficient number of them are available. The optimal thresholds and accessible spot counts can be computed using a high-fidelity microscopic vehicle trajectory simulation and an efficient search procedure. Unlike parking facilities, our numerical results show the throughput capacity per unit area of accessible PUDO facilities can be increased by constructing a relatively large number of accessible spots and allowing vehicles without handicap registration to use them subject to the threshold policies. In other words, it is optimal or near-optimal to make nearly all spots accessible and flexible when vehicle service times are short.
In Chapter 4, we consider the operational policy design problem for loading and unloading zones, also known as delivery bays in the urban context. The current and continuing surge in e-commerce and on-demand delivery services has contributed to a growing need for goods to be delivered directly to their end consumers. Furthermore, an increased demand for time-insensitive parcel deliveries as well as time-sensitive grocery and meal deliveries was stimulated during the COVID-19 pandemic when the mobility and movement of many people were limited or affected. One may expect the continuation of this trend due to the convenience and flexibility brought to consumers. This last step of goods delivery is often fulfilled at the curbsides or designated freight bays where delivery vehicles can be parked close to the final destinations. However, the sustainability and feasibility of exploiting the curbside to fulfill the delivery services are questionable, due to the various functions of the curb that compete over limited curb space. It is thus essential to envision designated spaces that serve as facilities for goods to be picked up or dropped off efficiently, with minimized effects on the local traffic. This work aims to provide computational models and methods that evaluate and characterize the optimal operational policies of loading and unloading facilities for goods delivery, passenger transportation, or a mix of both. The important operational decision to make is whether to admit or delay an arriving vehicle and where to assign an admitted vehicle. We specify the problem using a continuous-time Markov Decision Process and propose multiple low-fidelity models that are easier to solve and are upper bounds on the system performance. We motivate and propose near-optimal approximate policies that are computationally efficient. The approximate policies are validated using a microscopic trajectory-based simulation and compared against a benchmark policy.
Lastly, Chapter 5 presents an empirical study to understand how facility layout design decisions impact the average vehicle enter and exit maneuver times. Real vehicle maneuvers are recorded at selected parking and PUDO facilities. Various factors including spot dimensions and angles, sizes of vehicles, and the occupancy of neighboring spots during the maneuvers are also collected. A regression analysis is conducted to delineate the impacts of these factors on the averages of different vehicle maneuvers. The results and insights from this empirical work may inform the optimization of PUDO facility layout design decisions.
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Date
2024-07-27
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Dissertation