Phd Dissertation

Peer-to-peer and Collaborative Consumption of Supply in Transportation Systems

Publication Date

August 15, 2017

Abstract

Transportation systems have been traditionally operated on a First-Come-First-Served (FCFS) fashion. FCFS consumption of supply occurs because it is accepted as a natural paradigm when the operators have no individual-specific information that allows consideration of any other serving order, and when users are assumed not to communicate among themselves. Thus, FCFS behaves as a status quo policy that is generally considered as fair, since it is presumed that all users are treated equally. We know though, that there exists heterogeneity in users’ valuation of time and delay savings, and that the values may be different in different situations even for the same user. Taking advantage of smartphones and connected vehicle environments, it is now possible to include this user heterogeneity into operations in order to increase overall system efficiency and fairness, where efficiency refers to satisfaction of users. There are then possibilities of accomplishing this through exchanges among users with appropriate pricing, which can be determined by the users themselves to their satisfaction, so as to determine the order and extent of the utilization of supply. This new operational paradigm leads to collaborative consumption of supply.

This dissertation explores the idea of violating FCFS by allowing users to trade in real-time the part of supply that they effectively “own” while they are in a transportation system. This de-facto ownership originates from the space-time region which each user rightfully controls, either due to their physical presence or due to reservations such as after purchasing a future trip from an operator. Attempting to answer the question of what pricing scheme would be fair and acceptable, leads this dissertation to introduce for the first time in transportation literature, the fundamental economic concept of envy-freeness. It can be taken as a pricing scheme as well as a user-behavior model. A resource allocation is said to be envy-free, when no agent feels any other agent’s allocation to be better than their own, at the current price. An extension called dynamic envy-freeness is then developed for use in the domain of dynamic problems that the transportation field invariable pose, and a new family of envy-minimizing criteria are developed, namely the Constant Elasticity of Substitution Envy Intensity (CESEI) criteria, which strongly fits into the existing axiomatic body of Welfare Economics.

Several applications of collaborative consumption that breaks FCFS ordering are explored in this dissertation. First, the dissertation develops PEXIC, Priced EXchanges in Intersection Control, in which users can pay other users to reduce their waiting delays in a fair manner. This system is shown to be Pareto-efficient, envy minimizing and financially self-sustainable. Second, it studies new operational policies in highway control: parallel queue routing policies for bottleneck situations where the vehicles’ lane-queue selections are the results of trades, and queue-jumping operations for exit lanes where vehicles can take forward spots in a queue by paying the overtaken vehicles in a fair fashion that achieves queue stability. Third, it proposes Peer-to-peer (P2P) ride exchange in ridesharing systems, in which trip property rights are transferred to users in such a way that they can trade their rides between each other. Finally, the dissertation models a P2P ridesharing system as a dual role market exchange economy, introducing a truthful pricing scheme which includes High-Occupancy-Vehicle (HOV) lane savings and uses a novel min-cost max flow formulation that guarantees users a ride-back, a complementarity in preferences never explored before.

The research does not attempt any elaborate examination of the social equity implications of such exchange-based systems with non-FCFS operations, but identifies some of such key issues and presents pointers for further study. It does not purport to take an advocacy position on transforming the transportation system operations to the newer paradigms, nor does it examine all the regulatory complications. The research does, however, demonstrate through modeling and analysis results from a variety of applications, that better system efficiency and user satisfaction can be achieved with the use of the proposed paradigms.

Phd Dissertation

Essays in Transportation and Environmental Economics

Publication Date

September 15, 2017

Abstract

This thesis uses the tools of applied econometrics to study the impact of economic incentives on household welfare and decision-making and the environmental outcome of urban transportation policies in the U.S. and in developing countries.

Transportation is an essential component of day-to-day life. An extensive transportation system offers mobility, expanding individuals’ access to employment opportunities, agglomeration benefits to firms and employees, reduced trade costs, and an overall increase in productivity. The positive effects of an efficient transportation network in an economy are often accompanied by rising motorization rates. This, in turn, can lead to air pollution, road congestion, and increasing dependence on fossil fuels. In the past few decades, climate change concerns have made policymakers and governments agencies in both developed and developing countries incentivize improvement in fuel economy of vehicles as well as promote alternative fuel vehicles.

Alternative fuel vehicles currently arriving in the market offer better driving performance compared to their predecessors, and their market penetration is higher than before. However, most people still do not consider these alternative fuel vehicles as a substitute of traditional gasoline cars. Incentives offered to consumers to promote adoption have achieved varied results. The first chapter of the dissertation studies the stated vehicle transaction decisions of 3,154 survey respondents located in the state of California. While the effectiveness of policy incentives like tax credits and rebates is found to be more universal, the effect of High Occupancy Vehicle (HOV) lane permit or free parking benefit on adoption decision depends on the likelihood of the household being able to use the benefits. In addition, familiarity with an alternative fuel technology is found to be positively correlated with the preference for electric battery or hydrogen fuel cell vehicles. Prior ownership of a hybrid vehicle made the household more likely to purchase an alternative fuel vehicle in the future. This persistence in choice behavior can be attributed to heterogeneity among vehicle purchasers or considered as a sign of positive experience. Experience can reduce skepticism about alternative fuel vehicles and induce future adoption. Accounting for the number of years of ownership of alternative fuel vehicles, the results show that more experience has a positive effect on the probability of repurchase of the same or a newer technology vehicle. This result contributes towards a long standing debate of whether the incentives work only as a marketing mechanism or does it have any long term benefits. The positive correlation in preference pattern and the willingness to pay measures indicate that even if the price-based incentives work as a marketing mechanism they play an important role in initiating potential state dependence in purchase behavior to improve adoption in the long run.

In recent years, emerging economies like India and China have been experiencing the externalities related to increased motorization. Urbanization accompanied with increasing per capita income has led to a rise in private automobile demand. Historically, the infrastructure of major metropolitans in these emerging economies was not designed to support a sudden rise in the use of automobiles. As a result, a majority of these metropolitans suffer from congestion and pollution from greenhouse gas (GHG) emissions. The local government and policymakers in these economies are considering a variety of policies like to scrap old polluting vehicles, impose fuel standards, cordon tolls, and driving restrictions to address these issues. Driving restrictions has been implemented in several metropolitan cities in emerging economies like Beijing, China, Santiago, Chile, Mexico City, Mexico, S~{a}o Paulo, Brazil, Bogot

research report

New Methods for Monitoring Spatial Truck Travel Patterns in California Using Existing Detector Infrastructure

Abstract

This study developed a methodology to accurately estimate network-wide truck flows by leveraging existing point detection infrastructure, namely inductive loop detectors. The tracking model identifies individual trucks at detector locations using advanced inductive signatures and matches vehicle pairs at detector locations, using an extended form of the Bayesian classification model to estimate matching and non-matching probabilities of the vehicle pairs Several vehicle feature selection and weighting methods including Self Organizing Map and K-means clustering were applied to better identify individual vehicles from signature data. It was shown that the proposed extensive feature processing enhanced vehicle identification performance even among vehicle pools sharing similar physical configurations. The developed model was tested along an approximately 5.5-mile freeway segment on I-5 and CA-78 in San Diego, California where only 67 percent of the total trucks were observed at both up- and down-stream detector sites. Results showed balanced performances in exactness and completeness of matching with 91 percent of correct outcomes for multi-unit trucks

research report

New Methods for Monitoring Spatial Truck Travel Patterns in California Using Exisiting Dectector Infrastructure

Abstract

This study developed a methodology to accurately estimate network-wide truck flows by leveraging existing point detection infrastructure, namely inductive loop detectors. The tracking model identifies individual trucks at detector locations using advanced inductive signatures and matches vehicle pairs at detector locations, using an extended form of the Bayesian classification model to estimate matching and non-matching probabilities of the vehicle pairs Several vehicle feature selection and weighting methods including Self Organizing Map and K-means clustering were applied to better identify individual vehicles from signature data. It was shown that the proposed extensive feature processing enhanced vehicle identification performance even among vehicle pools sharing similar physical configurations. The developed model was tested along an approximately 5.5-mile freeway segment on I-5 and CA-78 in San Diego, California where only 67 percent of the total trucks were observed at both up- and down-stream detector sites. Results showed balanced performances in exactness and completeness of matching with 91 percent of correct outcomes for multi-unit trucks

MS Thesis

Supply-demand forecasting for a ride-hailing system

Abstract

Ride-hailing or Transportation Network Companies (TNCs) such as Uber, Lyft and Didi Chuxing are gaining increasing market share and importance in many transportation markets. To estimate the efficiency of these systems and to help them meet the needs of riders, big data technologies and algorithms should be used to process the massive amounts of data available to improve service reliability. The model developed predicts the gap between rider demands and driver supply in a given time period and specific geographic area using data from Didi Chuxing, the dominant ride-hailing company in China. The data provided includes car sharing orders, point of interest (POI), traffic, and weather information. A passenger calls a ride (makes a request) by entering the place of origin and destination and clicking “Request Pickup” on the Didi phone based application. A driver answers the request by taking the order. Our training data set contains three consecutive weeks of data in 2016, for large Chinese city which is referred to as City M. Though the training set is relatively small when compared to the whole of Didi’s ride sharing market, it is large enough so that patterns can be discovered and generalized. These data were made available to researchers and entrepreneurs by Didi after removal of some identifying information. 

research report

CTM-based optimal signal control strategies in urban networks

Abstract

This research introduces a novel analytical framework in deriving invariant averaged models for signalized intersections in urban networks, using the capability of the cell transmission model (CTM) to capture the detailed traffic dynamics such as the formation, propagation, and dissipation of congestion arising at network junctions. Generally, the CTM formulates the optimization problem as a mixed-integer linear-programming (MILP) problem, which introduce many binary variables for large-scale urban networks and is difficult to solve. The approach aims to derive invariant averaged models to eliminate the binary variables introduced by the traffic signals. For the purpose of simplicity, the approach emphasizes on a signalized linear junction connecting one upstream link with one downstream link. Using the Cell Transmission Model (CTM) simulation on a signalized ring road, the authors demonstrate that the invariant averaged model is a reasonable approximation to the original supply-demand model with binary signals. Due to the existence of merging behaviors, the authors introduce two new terms while deriving the averaged model: Effective Demand and Merging Priority. With these two new terms, the authors follow similar procedures as those in the linear junction, and derive the corresponding invariant averaged model for the merging junction. The authors further show that the derived averaged model for the signalized linear junction is just one special case of the one for the signalized merging junction with empty demand in one of the upstream links.

research report

Policy and Literature Review on the Effect Millennials Have on Vehicle Miles Traveled, Greenhouse Gas Emissions, and the Built Environment

Abstract

Vehicle travel has reduced substantially across all demographics in the 2000s, but millennials or young adults born between 1985-2000 stand out as the group that has reduced vehicle travel the most. This reduction of travel among millennials is known as the millennial effect. This policy and literature review discusses insights from recent policy reports and literature regarding the millennial effect and identifies the prominent themes and gaps in knowledge. The first section reviews existing research on the millennial effect on vehicle miles traveled (VMT). The second section discusses the influence of the built environment on the travel and activities of the millennial generation. The third section highlights scenarios describing the millennial effect’s potential magnitude and identifies topics for consideration in future scenario planning efforts. The final section discusses the uncertainty that exists regarding the future behavior of millennials and their influence on vehicle miles traveled and greenhouse gas emissions.

research report

Air Quality and Greenhouse Gas Benefits of an Advanced Low-NOx Compressed Natural Gas Engine in Medium- and Heavy-Duty Vehicles in California

Abstract

The goal of this research is to assess the greenhouse gas (GHG) emissions and air quality (AQ) impacts of transitions to advanced low‐NOx Compressed Natural Gas (CNG) engines in medium-duty vehicle (MDV) and heavy-duty vehicle (HDV) applications in California with a particular emphasis on renewable natural gas (RNG) as a fueling pathway. To evaluate regional air quality impacts in 2035, pollutant emissions from all end-use sectors are projected from current levels and spatially and temporally resolved. Scenarios are constructed beginning with both a conservative (Base Case) and more optimistic (SIP) case regarding advanced vehicle technology and fuel integration to provide a spanning of potential impacts. To capture the impact of seasonal dynamics on pollutant formation and fate, two modeling periods are conducted including a winter and summer episode. To estimate the potential GHG impacts of transitions to advanced CNG engines in HDV and MDV, scenarios are evaluated under various assumptions regarding fuel pathways to meet CNG demand from a life cycle perspective. Scenarios are compared to the baseline cases assuming (1) all CNG is provided from conventional fossil natural gas and (2) under a range of possible resource availabilities associated with renewable natural gas and renewable synthetic natural gas (RSNG) from in-state resources. Key findings include: i) expanding the deployment of advanced CNG MDV and HDV can reduce summer ground-level ozone concentrations and ground-level PM2.5 in key regions of California; ii) the largest AQ benefits are associated with reducing emissions from HDV; iii) in-state renewable natural gas pathways can meet the CNG demand estimated for both baseline cases; iv) in-state resources are unable to entirely meet CNG demand for the high total CNG demand estimated for the majority of Base alternative cases, and v) advanced CNG HDV and MDV can moderately reduce GHG emissions if fossil natural gas is used (14 to 26%).

research report

Transit Investment Impacts on Land Use Beyond the Half-Mile Mark

Abstract

This project examines the impacts of light rail transit investments on broader vicinity areas in Los Angeles County. This project found that the land use impacts of public transit investments are not necessarily confined to the half-mile boundary around station areas, although substantial variation exists by transit line.  While the areas beyond the half-mile mark were often excluded from conventional transit-oriented planning processes, these areas show a distinct pattern of land use transformation. Areas beyond the half-mile mark had a higher rate of development for several urban purposes, particularly after a few years have elapsed since the opening of nearby transit lines/stations.