Optimization and Technology-Based Strategies to Improve Public Transit Performance Accounting for Demand Distribution

Public transit is important to societies worldwide. The operation of public transit systems is generally associated with great benefits for the users, but there are also cases in which these systems demonstrate inefficient performance. Quantifying transit performance is an important area of research over the last decades. This dissertation presents models to improve transit system performance through optimization techniques and new technologies, recognizing the effects of non-uniform distribution of demand over space and time. The contributions span fixed route transit services and on-demand transit, as well as models for flexible transit operations that lie in between. Regarding fixed route systems, a methodology is proposed to estimate the number of passengers being left-behind subway train vehicles due to overcrowding. Methods to identify appropriate time periods and locations for studying this phenomenon are presented. The effects of overcrowding on passenger waiting times are also investigated. The challenging case of transit networks where passengers tap-in only upon entrance is analyzed, adding a new methodology to a very short list of similar studies and enhancing previous work in this field. For demand responsive systems, this dissertation focuses on optimizing the operation of paratransit services through coordination with alternative providers in order to decrease high operating costs of such a service. The analysis includes a heuristic-based method. The proposed model is more detailed than existing aggregated methods and is able to perform well in high demand levels, unlike existing exact approaches. This part of the dissertation also assists in making transportation network companies a complementary part of public transit, rather than a competitor. Finally, flexible transit systems are studied to identify the operational and demand related characteristics of a service area that could serve as indicators of such systems\u27 efficient performance. The focus here is on route deviation flexible services. Continuous approximation is used to model this flexible system. A new optimized hybrid transit system with elements of both fixed route and flexible services is proposed. Finally, it is highlighted that the current COVID-19 pandemic has proven the need for public transit systems that could be adjusted to accommodate changes in transit demand

Impact assessment of autonomous DRT systems

The market entrance of shared autonomous vehicles (SAV) may have disruptive effects on current transport systems and may lead to their total transformation. For many small and medium-sized cities, a full replacement of public transport services by these systems seems to be possible. For a transport system operator, such a system requires a bigger fleet of vehicles than before, however, vehicles are less expensive and fewer staff is needed for the actual operation. In this paper, we are using a simulation-based approach to evaluate the service quality and operating cost of a demand responsive transit (DRT) system for the city of Cottbus (100 000 inhabitants), Germany. The simulation model used is based on an existing MATSim model of the region that depicts a typical work day. Results suggest, that the current public transport system may be replaced by a system of 300 to 400 DRT vehicles, depending on their operational mode. Compared to previous, schedule based public transport, passengers do not need to transfer, and their overall travel times may be reduced significantly. Results for the cost comparison are preliminary, but results suggest that an autonomous DRT system is not necessarily more expensive than the current public transport system

Transportation network companies as cost reduction strategies for paratransit

Paratransit service is an auxiliary type of public transportation provided for people with disabilities and older adults. Federal ADA regulations require all transit agencies receiving federal funding to provide paratransit service, but the per trip cost to transit operators is extremely expensive. Many transit agencies are looking for ways to reduce costs without limiting services. For many agencies, this results in providing the minimum services as required by ADA regulations. However, Boston’s Massachusetts Bay Transit Authority (MBTA) has taken a different approach to cost reduction by entering into one of the first partnerships with transportation network companies. In September 2016, MBTA’s paratransit service, The Ride, began a partnership with both Uber and Lyft as a cost reduction strategy for paratransit provision. Since the beginning of the partnership, MBTA has been able to reduce costs of providing paratransit while maintaining the same level of service. This report will examine the benefits and limitations of such partnerships between transit agencies and transportation network companies, using MBTA’s The Ride partnership as an example for potentially successful partnerships throughout the United States.Community and Regional Plannin

Arterial traffic signal optimization: a person-based approach

This paper presents a traffic responsive signal control system that optimizes signal settings based on minimization of person delay on arterials. The system's underlying mixed integer linear program minimizes person delay by explicitly accounting for the passenger occupancy of autos and transit vehicles. This way it can provide signal priority to transit vehicles in an efficient way even when they travel in conflicting directions. Furthermore, it recognizes the importance of schedule adherence for reliable transit operations and accounts for it by assigning an additional weighting factor on transit delays. This introduces another criterion for resolving the issue of assigning priority to conflicting transit routes. At the same time, the system maintains auto vehicle progression by introducing the appropriate delays for when interruptions of platoons occur. In addition to the fact that it utilizes readily available technologies to obtain the input for the optimization, the system's feasibility in real-world settings is enhanced by its low computation time. The proposed signal control system was tested on a segment of San Pablo Avenue arterial located in Berkeley, California. The findings have shown the system's capability to outperform static optimal signal settings and have demonstrated its success in reducing person delay for bus and in some cases even auto users

Chapter 13 - Sharing strategies: carsharing, shared micromobility (bikesharing and scooter sharing), transportation network companies, microtransit, and other innovative mobility modes

Shared mobility—the shared use of a vehicle, bicycle, or other mode—is an innovative transportation strategy that enables users to gain short-term access to transportation modes on an “as-needed” basis. It includes various forms of carsharing, bikesharing, scooter sharing, ridesharing (carpooling and vanpooling), transportation network companies (TNCs), and microtransit. Included in this ecosystem are smartphone “apps” that aggregate and optimize these mobility options, as well as “courier network services” that provide last mile package and food delivery. This chapter describes different models that have emerged in shared mobility and reviews research that has quantified the environmental, social, and transportation-related impacts of these services

The importance of information flows temporal attributes for the efficient scheduling of dynamic demand responsive transport services

The operation of a demand responsive transport service usually involves the management of dynamic requests. The underlying algorithms are mainly adaptations of procedures carefully designed to solve static versions of the problem, in which all the requests are known in advance. However there is no guarantee that the effectiveness of an algorithm stays unchanged when it is manipulated to work in a dynamic environment. On the other hand, the way the input is revealed to the algorithm has a decisive role on the schedule quality. We analyze three characteristics of the information flow (percentage of real-time requests, interval between call-in and requested pickup time and length of the computational cycle time), assessing their influence on the effectiveness of the scheduling proces

Testing demand responsive shared transport services via agent-based simulations

Demand Responsive Shared Transport DRST services take advantage of Information and Communication Technologies ICT, to provide on demand transport services booking in real time a ride on a shared vehicle. In this paper, an agent-based model ABM is presented to test different the feasibility of different service configurations in a real context. First results show the impact of route choice strategy on the system performance

Optimal Design of Demand-Responsive Feeder Transit Services

The general public considers Fixed-Route Transit (FRT) to be inconvenient while Demand-Responsive Transit (DRT) provides much of the desired flexibility with a door-to-door type of service. However, FRT is typically more cost efficient than DRT to deploy. Therefore, there is an increased interest in flexible transit services including all types of hybrid services that combine FRT and pure DRT. The demand-responsive feeder transit, also known as Demand-Responsive Connector (DRC), is a flexible transit service because it operates in a demand-responsive fashion within a service area and moves customers to/from a transfer point that connects to a FRT network. In this research we develop analytical models, validated by simulation, to design the DRC system. Feeder transit services are generally operated with a DRC policy which might be converted to a traditional FRT policy for higher demand. By using continuous approximations, we provide an analytical modeling framework to help planners and operators in their choice of the two policies. We compare utility functions of the two policies to derive rigorous analytical and approximate closed-form expressions of critical demand densities. They represent the switching conditions, that are functions of the parameters of each considered scenario, such as the geometry of the service area, the vehicle speed and also the weights assigned to each term contributing to the utility function: walking time, waiting time and riding time. We address the problem faced by planners in determining the optimal number of zones for dividing a service area. We develop analytical models representing the total cost functions balancing customer service quality and vehicle operating cost. We obtain close-form expressions for the FRT and approximation formulas for the DRC to determine the optimal number of zones. Finally we develop a real-case application with collected customer demand data and road network data of El Cenizo, Texas. With our analytical formulas, we obtain the optimal number of zones, and the times for switching FRT and DRC policies during a day. Simulation results considering the road network of El Cenizo demonstrate that our analytical formulas provide good estimates for practical use