Inferring trip occupancies in the rise of ride-hailing services

Singapore National Research Foundation under International Research Centre @ Singapore Funding Initiativ

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

Advances in Public Transport Platform for the Development of Sustainability Cities

Modern societies demand high and varied mobility, which in turn requires a complex transport system adapted to social needs that guarantees the movement of people and goods in an economically efficient and safe way, but all are subject to a new environmental rationality and the new logic of the paradigm of sustainability. From this perspective, an efficient and flexible transport system that provides intelligent and sustainable mobility patterns is essential to our economy and our quality of life. The current transport system poses growing and significant challenges for the environment, human health, and sustainability, while current mobility schemes have focused much more on the private vehicle that has conditioned both the lifestyles of citizens and cities, as well as urban and territorial sustainability. Transport has a very considerable weight in the framework of sustainable development due to environmental pressures, associated social and economic effects, and interrelations with other sectors. The continuous growth that this sector has experienced over the last few years and its foreseeable increase, even considering the change in trends due to the current situation of generalized crisis, make the challenge of sustainable transport a strategic priority at local, national, European, and global levels. This Special Issue will pay attention to all those research approaches focused on the relationship between evolution in the area of transport with a high incidence in the environment from the perspective of efficiency

Mobility on Demand (MOD) Sandbox Demonstration: Los Angeles County and Puget Sound First and Last Mile Partnership with Via Evaluation Report

This report presents the results of an independent evaluation of the Los Angeles County and Puget Sound First and Last Mile Partnership with Via demonstration, part of the Federal Transit Administration (FTA) Mobility on Demand (MOD) Sandbox program. The demonstration, a joint pilot project in Los Angeles County and the Puget Sound region, executed an on-demand service in partnership with Via providing first- and last-mile access to transit stations within the Los Angeles and Seattle metropolitan regions. Upon launch, customers were able to request subsidized Via rides to or from the participating transit stations within specified zones and times of the day. The Los County Metropolitan Transportation Authority (LA Metro), King County Metro, Sound Transit, and Via (an on-demand transportation network company or TNC) worked together to develop and deploy two analogous pilot projects designed to test the viability of public-private partnerships to deliver access to and from core lines within the public transit networks. Three zones were selected in the Los Angeles region, and five zones were selected in the Puget Sound region. The evaluation of this MOD Sandbox project included 13 hypotheses that explored a number of potential impacts from the project, including mobility, accessibility, public transit ridership, fuel consumption, safety, costs, and lessons learned

Data-Driven Design of On-Demand Multimodal Transit Systems

Across the United States, public transit agencies are facing trends of decreasing ridership. Especially during and since the COVID-19 pandemic, reduced ridership caused many agencies to have significant budget deficits due the to the high-fixed cost of traditional transit systems. Many transit planners are exploring network redesign to address these changing ridership patterns and their budget deficits. On-demand services have previous been used in smaller cities, and more rural areas and for paratransit services, but now larger cities are starting to explore using on-demand services to help supplement their fixed route services to create a more accessible and scalable system. On-demand Multimodal Transit Systems may be an accessible, scalable solution for first and last mile issues that often plague many transportation systems. Chapter 2 presents a novel methodology to help transit agencies with their tactical planning for post-game ridership of large events. The methodology has three main steps: (1) predicting the total post-game ridership; (2) combining the total prediction with historical trends to forecast the passenger flow curve at nearby stations after the game; and (3) estimating the required train frequencies to serve these customers with minimal passengers left behind by each train. Additionally, this chapter proposes a suite of data-driven techniques that together create a data-driven pipeline to exploit Automated Fare Collection (AFC) data for evaluating, anticipating, and managing the performance of transit systems. This chapter includes a case study where the proposed pipeline is used to generate an adjusted train schedule for the post-game period and simulated with the rail ridership data from the Metropolitan Atlanta Rapid Transit Authority (MARTA). The simulation results highlight how the proposed schedules based on the estimated required post-game train frequencies could significantly improve post-game congestion and wait time. Chapter 3 studies the resiliency during a pandemic of On-Demand Multimodal Transit Systems (ODMTS), a new generation of transit systems that combine a network of high-frequency trains and buses with on-demand shuttles to serve the first and last miles and act as feeders to the fixed network. A limit is imposed on the number of passenger transfers with a new network design model leveraging a transfer-expanded graph. It presents a case study for the city of Atlanta and evaluates ODMTS for multiple scenarios of depressed demand and social distancing representing various stages of the pandemic. The case study relies on a real data from MARTA, an optimization pipeline for the design of ODMTS, and a detailed simulation of these designs. The case study demonstrates how ODMTS provide a resilient solution in terms of cost, convenience, and accessibility for this wide range of scenarios. Chapter 4 addresses to what extent On-Demand Multimodal Transit Systems (ODMTS) in combination with Dedicated Bus Lanes (DBLs) improve the attractiveness of public transit under various congestion scenarios. Previous case studies have shown that ODMTS may simultaneously improve travel time, reduce system cost, and attract new passengers compared to existing fixed-route systems. However, none of these studies include the effect of congestion on the travel times, adoption, and cost of the ODMTS. This paper introduces new methods to help model the ODMTS under various congestion scenarios as part of a case study in the Metro Atlanta Area, analyzing the system both with and without DBLs. The results show that an ODMTS with DBLs and synchronization could make public transit a viable, accessible option for many more people, especially in currently underserved areas. Higher congestion actually leads an ODMTS with DBLs to be faster than a direct trip by car in some cases by mitigating the negative impact of congestion on the most congested corridor. Chapter 5 presents a novel mixed-integer program (MIP) formulation to incorporate bus line design into the network design problem for On-Demand Multimodal Transit Systems (ODMTS) that allows the model to accurately capture wait time and transfer costs in addition to travel time and vehicle costs. To solve large-scale instances, a two-stage reformulation is presented where the first-stage problem decides which bus arcs to open and decides which arcs immediately follow and the second-stage problem decides the multimodal path for each individual trip. The solution method is based on the Benders decomposition method and uses disaggregated subproblems and Pareto-optimal cuts. This chapter includes a case study of the Metro Atlanta Area with instances that have up to 43,000 unique trips and hundreds of bus arcs for potential lines. The results show there was a significant reduction in number of transfers for individuals when considering the bus lines as part of the network design phase.Ph.D

Transportation Behavioral Data and Climate Change

In 2017, transportation became the largest single source of greenhouse gas emissions from the United States. Globally, the 2014 Intergovernmental Panel on Climate Change report found that, without far more aggressive policies, “transportation emissions could increase at a faster rate than emissions from other energy end use sectors” reaching 12 Gt CO2-eq/year by 2050 (Sims et al., 2014). The overwhelming challenge of combatting these emissions is made far more difficult by the fact that so little is known about transportation behavior. To use a cliché – if we can’t measure it, we can’t manage it. And transportation must be managed if we are to avoid the most catastrophic consequences of climate change. In this dissertation, I propose that better data collection is necessary to achieve reduction of transportation-related emissions. Happily, advances in technology make this more feasible today than at any time in the past. The costs of massive computing resources have gone down, the world is swarming with mobile devices like smartphones and connected cars collecting massive (if messy) amounts of data, and new techniques in data science and machine learning have emerged to help get clean answers out of all that data in a privacy-appropriate manner. In some cases, these new techniques will displace older ones. In other cases, the old ways have inherent advantages. In other cases yet, fusing new and old techniques will yield the most productive results.In Chapter One, I lay out a framework to organize the types of transportation behavioral data that must be collected regularly to adequately measure and manage transportation’s impact on climate. This builds on classic climate impact frameworks, adapting them to the particular measurement challenges presented by transportation. In Chapter Two, I provide a history of US transportation data collection since World War II as well as a review of traditional, modern, and emerging transportation data collection technologies. I then map each technology onto each behavioral data collection need identified in Chapter One, matching each behavior to the best respective data collection technique.Chapters Three and Four provides an example of analysis done using the traditional data collection techniques, notably Household and Commercial Travel Surveys, to explore changes in PMT related to shopping and retail freight since 1969, as well as freight for fuel transportation. They demonstrate and take advantage of the key benefits of traditional techniques: that they go back in history, that they collect clearly stated trip purposes, vehicle occupancies, demographics (including gender, an important demographic but particularly difficult to deduce from the new data collection sources), trip distances, chaining behavior, commodities logged, and more. As it turns out, these benefits are critical: the historical trends of the past 40 years allow behavioral insight that would not have been possible with a shorter term study, and gender dynamics are key to understanding the behaviors at hand. However, the analysis in Chapters Three and Four also highlights some of the key limitations of survey-based analysis. The fact that data was only collected every five to ten years severely limits the analysis, such as limiting the exploration that can be done on the impacts of the Great Recession. In addition, fallibilities in human memory are especially pronounced in short trips, trip chains, and non-work related trips, all of particular importance to this study. Chapters Five lays out theoretically, and then Chapter Six demonstrates via case study in India, how personal GPS diary devices can be used to log detailed data about individual trips. It demonstrates the key benefit of this data – highly individualized characteristics. Taking the example of vehicle electrification, this chapter demonstrates two ways such granular data is important: in one example, such data to give feedback to an individual to influence their car buying behavior. In the second, the granularity found with this new data collection techniques reveals the importance of highly localized policy making and emissions modeling based on driving patterns in different cities.Chapter Seven uses the emerging technology of mass amounts of locational data, collected passively via smart phones, to explore how urban density at home and work interacts with total, work-related, and non-work-related miles driven. This demonstrates the great strength of this type of data – massive sample size combined with high spatial granularity and longitudinal data collection. These strengths enable the analysis at statistically meaningful scale of patterns across many geographies, individuals, and times of year. Thus, this data can shed light on questions about the relationship of density and miles travelled which previously have not been answered conclusively due to data constraints

Paying for public transportation : the optimal, the actual, and the possible

Thesis (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 2007.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.:June 2007."Includes bibliographical references (p. 110-121).Passenger transportation poses challenges to American cities in the form of air pollution, traffic congestion, auto collisions, and barriers to mobility. Public transit has the potential to be part of a solution to these urban problems, yet transit agencies across the country clamor for more resources. Transit finance in the U.S. is heterogeneous, and rarely approached with a comprehensive view of transit's social benefits. This thesis suggests a framework for a more rational magnitude and incidence of public transit funding based on a more comprehensive view of transit's social benefits. I take up the case of the Chicago metropolitan region and quantify the transit system's major emissions, safety, congestion, and mobility benefits. Next, I survey and highlight current practices in transit finance from other cities in North America and Western Europe. Finally, I assess the size, structure, and distribution of burden of Chicago's current transit funding status quo against theoretical and practical principles of transit funding and offer a range of financing alternatives to solve the current fiscal crisis in Chicago.(cont.) I find evidence that the social benefits of public transportation in Chicago outweigh its costs, suggesting that preserving transit services there is justifiable. Transit's benefits accrue to a variety of jurisdictions in diverse and measurable ways which the current funding structure does not approximate. I find evidence that of the multiple beneficiaries of transit in the region, the subsidy structure in Chicago disproportionately benefits auto drivers who receive significantly more congestion benefits than they pay for. Last, I propose several policy options to increase public subsidy to transit in Chicago, and suggest that one particularly theoretically appealing alternative may be to establish tolls on existing roadways.by Justin David Antos.M.C.P

Rural implementation of connected, autonomous and electric vehicles

Connected, autonomous and electric vehicles (CAEV) are at the forefront of transport development. They are intended to provide efficient, safe and sustainable transport solutions to solve everyday transport problems including congestion, accidents and pollution. However, despite significant industry and government investment in the technology, little has been done in the way of exploring the implementation of CAEVs in rural scenarios. This thesis investigates the potential for rural road CAEV implementation in the UK. In this work, the rural digital and physical infrastructure requirements for CAEVs were first investigated through physical road-based experimentation of CAEV technologies. Further investigations into the challenges facing the rural implementation of CAEVs were then conducted through qualitative consultations with transport planning professionals. Quantitative and qualitative analysis of these investigations revealed a need for better rural infrastructure, and an overall lack of understanding regarding CAEVs and their rural implementation requirements amongst the transport planning industry. The need for a measurement tool for transport planners was identified, to expose the industry to, and educate them about, CAEVs and their rural potential. As a result, a CAEV Rural Transport Index (CARTI) is proposed as a simple measurement tool to assess the potential for rural CAEV implementation. The CARTI was implemented, and its effectiveness tested, through further consultation with transport planning professionals. The results indicate the potential for the CARTI to be used as a component of decision-making processes at both local authority and national levels. In conclusion, effective rural CAEV implementation relies on transport planners having a strong understanding of rural community transport needs, the solutions CAEV technologies can offer and the supporting infrastructure they require. Further, the CARTI was found to be an effective tool to support the development of this required understanding and recommendations have therefore been made for its future development

Rural implementation of connected, autonomous and electric vehicles

Connected, autonomous and electric vehicles (CAEV) are at the forefront of transport development. They are intended to provide efficient, safe and sustainable transport solutions to solve everyday transport problems including congestion, accidents and pollution. However, despite significant industry and government investment in the technology, little has been done in the way of exploring the implementation of CAEVs in rural scenarios. This thesis investigates the potential for rural road CAEV implementation in the UK. In this work, the rural digital and physical infrastructure requirements for CAEVs were first investigated through physical road-based experimentation of CAEV technologies. Further investigations into the challenges facing the rural implementation of CAEVs were then conducted through qualitative consultations with transport planning professionals. Quantitative and qualitative analysis of these investigations revealed a need for better rural infrastructure, and an overall lack of understanding regarding CAEVs and their rural implementation requirements amongst the transport planning industry. The need for a measurement tool for transport planners was identified, to expose the industry to, and educate them about, CAEVs and their rural potential. As a result, a CAEV Rural Transport Index (CARTI) is proposed as a simple measurement tool to assess the potential for rural CAEV implementation. The CARTI was implemented, and its effectiveness tested, through further consultation with transport planning professionals. The results indicate the potential for the CARTI to be used as a component of decision-making processes at both local authority and national levels. In conclusion, effective rural CAEV implementation relies on transport planners having a strong understanding of rural community transport needs, the solutions CAEV technologies can offer and the supporting infrastructure they require. Further, the CARTI was found to be an effective tool to support the development of this required understanding and recommendations have therefore been made for its future development