The incorporation of alternative fuel vehicles has been essential in reducing emissions in the transportation sector. Particularly to heavy-duty trucks, zero-emission technologies are becoming more attractive. However, batteries and fuel cells still face a long way until they became became a viable solution in terms of price, autonomy, weight, and infrastructure. An interim solution is the use an overhead catenarysystem, also known as eHighway. The pilot project demonstrated the feasibility of the eHighway system; however, the literature exploring this type of technology is lacking. This dissertation aims to cover this literature gap and propose a new framework to comprehensively explore the aspects of an eHighway implementation in terms of optimal placement, effects on the well-to-wheel (WTW) emissions, and impacts on the power grid. This methodology was applied to a California model using data from the California Statewide Freight Forecasting Model.
First, we defined the optimal eHighway placement to maximize vehicle miles traveled in the system or minimize emissions around disadvantage communities in four different scenarios for the years of 2020 and 2040. This process shows that most eHighways would be located along the I-5 or close to ports to maximize vehicle miles traveled or in Central Valley to maximize the benefit for disadvantage communities.
Second, we estimated the WTW emissions for heavy-duty truck according to the truck fuel type for each of the scenarios with adoption rates from 25% to 100%. The total emissions in terms of CO2 and NOx were compared to a scenario without eHighway. All the eHighway scenarios for 2020 and 2040 reduced the total WTW heavy-duty truck emissions. The best-case scenario for 2020, with 500 miles of total eHighway length and adoption rate of 100%, reached a reduction of almost 8% in CO2 emission and over 20% of NOx. The same scenario showed a reduction of 16% in CO2 and 20% of NOx for the year 2040.
Finally, we analyzed the impacts of the eHighway energy demand on the state’s power grid. We showed that some of the systems would require up to 1 MWh of daily energy from some power substation. However, due to the unavailability of public data on California’s power grid, we could not draw conclusions in terms of the ability of these substation to handle such demand.
These results show the applicability of the proposed methodology for the deployment and impacts of the eHighway system. Furthermore, although there are other aspects to be considered before large-scale implementation of the eHighway system (e.g., costs), the results presented in this study support the deployment of an eHighway system in California to support the urgent need for making road freight transport more sustainable.
Complex travel behavior places travel in a broader context than in the conventional single-trip based approach. The activity-based approach provides an analysis framework that positions travel decisions as dependent on a collection of activities that form an agenda for participation and, therefore, cannot be properly analyzed on individual trip basis. The basic units of analysis for activity-based approaches are tours, which can be defined as sequences of trips and activities that begin and end at the same location. In this dissertation, I apply a tour-based approach to analyze complex travel behavior of individuals from three broad perspectives: environmental, technological, and economical.
First, I examine the complex travel behavior of workers, who utilize a sustainable transport option, namely public transit. I identify dominant patterns of work tours — tours that contain at least one work activity and have at least one link made by public transit — and analyze the factors that determine tour choice using Structural Equation Modeling (SEM). The results obtained by using the 2017 National Household Travel Survey (NHTS) dataset suggest that 80 percent of work tours consist of seven dominant tour patterns and that tour choice is influenced by a set of socio-demographic, built environment, and activity-travel characteristics. Second, the complex travel behavior of people who use technology-enabled ride-hailing services, such as Uber/Lyft, is explored. In particular, I identify heterogeneous groups of ride-hailing users by using Latent Class Analysis, analyze the activity-travel patterns of each of these groups, and discuss the ramifications of that behavior to policy directives.
Lastly, I explore the travel behavior of workers, again in terms of tours, when they are exposed to an economic downturn, the 2007-2009 great recession. I apply multi-group SEM to analyze changes in tour choice during the recession (2009) compared to pre- (2006) and post-recession (2012) years. Using American Time Use Survey data, this study shows that activity-travel relationships and their role in tour choices differed significantly in the recession year. The results of this study provide insights into potential changes in worker’s travel demand during a recession, which would contribute to building better pattern choice sets in tour-based models.
The common thread throughout this dissertation is the development of a framework for analyzing complex travel behavior under disruptive changes due to environment, technology, and economics forces.
High-occupancy vehicle (HOV) lanes are those reserved for cars with a minimum of two or three occupants and other qualified vehicles. However, some HOV lanes could be
underutilized, even when the corresponding general purpose (GP) lanes on the same roads are congested. A type of relatively recent congestion pricing strategy is realized with the high-occupancy toll (HOT) lane, where single-occupancy vehicles (SOVs) can pay a price to use HOV lanes during peak periods. The purpose of this study is to propose control theoretic approaches to congestion pricing for HOT lanes. This dissertation focuses on three operation objectives: (1) maintaining a certain level of service on the HOT lanes; (2) improving the overall system performance; and (3) maximizing revenue for the operators. Inspired by the work of (Yin and Lou, 2009), this study first proposes a simultaneous estimation and control method for a freeway with HOT and GP lanes. An integral controller is applied to estimate the average value of time (VOT) of SOVs, and the dynamic prices are calculated based on the logit model. The closed-loop system is proved to be stable and guaranteed to converge to the optimal state both analytically and numerically. Two convergence patterns, Gaussian or exponential, are revealed. The effect of the scale parameter in the logit model is also examined.
The heterogeneity of SOVs is essential for determining pricing schemes for HOT lanes. A vehicle-based user equilibrium (UE) principle is proposed to incorporate heterogenous SOVs. A general lane choice model is derived based on the characteristics of the logit and the vehicle-based UE principle. We obtain an insight regarding
the pricing schemes by analytically solving the optimal dynamic prices with constant demands of HOVs and SOVs. Then, we design a feedback controller to determine the dynamic prices without knowing SOVs’ lane choice models, but to satisfy the two control objectives: maximizing the flow-rate but not forming a queue on the HOT lanes.
Agencies have stated that revenue maximization should generally coincide with the optimization of freeway performances. In order to verify this statement, an optimal control problem is formulated to find the pricing scheme that maximizes the revenue for HOT lane operators. Numerical results show that there is conflict between those two
objectives. Operators need to make different strategies based on the traffic demand patterns. It has long been known that drivers’ departure time choice behavior is one fundamental cause of congestion. Further in this dissertation, pricing schemes are proposed to consider both lane choice equilibrium and departure time equilibrium. It turns out that flat pricing schemes are able to meet the following three constraints: (1) the nontransfer disutility (NTD) is minimized; (2) the cost for each HOV is not worse-off; and (3) the costs for each nonswitching and switching SOV are the same. We show that different revenue requirements lead to different pricing schemes.
Smart Urban Mobility in the future demands a paradigm shift. Transportation supply needs to be designed to incorporate individual-level preferences in an era of readily-
available information about other users and network performance. It is, therefore, reasonable to expect that an individual would have information to compare her transportation allocation with other users. For individuals having the same goal (e.g., the shortest path to the destination from the same departure location and time), the peer to peer comparison may induce ‘envy’ if the user perceives his/her assigned travel option to be worse than that of his/her peers.
In turn, a user adjusts his/her travel options until he/she does not feel envy. This concept is an extension of the well-known travel behavior assumption called “User
Equilibrium”. Existing behavior models, however, do not allow users to compare their allocations with others. Furthermore, it is assumed that users have perfect information about their own alternative and all users are homogeneous.
This dissertation is dedicated to modeling a smart shared-mobility system, which accounts for individual
level of allocation. More specifically, we focus on the optimization of the allocation problem to achieve both system-wide efficiency and minimum envy among individuals. We consider envy to be an important allocation aspect in the transportation system. Maximizing the efficiency of a system necessarily brings about some level of unfairness where some users are allocated to inferior alternatives. When users having superior alternatives can compensate the envy of groups having inferior alternatives, an envy-free state can be achieved—which can be shown to be Pareto efficient state. Using a combination of pricing and incentives, we propose an optimization model to arrive at this new equilibrium.
This research has significant contributions in that the proposed model provides a framework to combine system-wide objectives with individual users’ utility objectives. Furthermore, we consider user heterogeneity, which has not been researched in the general area of transportation assignment. The proposed optimization model can be applied to pricing strategies both for commercial and public agencies, who have real-time information about customer characteristics and system performance.
Numerical results obtained from running our optimization model on both toy and real networks show that the proposed model converges to both envy-free and system optimum states with appropriate allocation and pricing schemes. Our findings show that the proposed smart mobility system technically works efficiently without governmental subsidy since the budget-balance mechanism trades off credits among users. In addition, the level of user heterogeneity affects the amount of credits charged or disbursed.
Society has an interest in reducing pollutants emitted from the vehicles used for transporting people and goods. The main goal of heavy-duty natural gas vehicle (NGV) incentive projects is to offer upfront monetary incentives to reduce greenhouse gas emissions and the production of regulated pollutants in the state. However, these incentives are often based on vehicle weight and do not account for environmental impacts. In addition, although heavy-duty NGVs are being used in a variety of vocation types, conventional emission models only support a limited number of these vocation types. Because of this, it is challenging to assess the precise impacts of the heavy-duty NGV (HD NGV) adoption and predict the specific environmental benefits per given operational conditions and vocation type. If government agencies realize the environmental benefits of alternative fuel vehicles (AFVs), like NGVs, with respect to vocation type and operating characteristics, it would be beneficial to design cost-effective incentive structures and implementation plans. This study primarily focused on the operational characteristics and environmental impacts of the HD NGVs incentivized in California. This study conducted pattern clustering and classification analyses to obtain drive mode compositions (DMC) over duty cycles and showed the heterogeneity of operational and emission characteristics of the vocational HD NGVs. The vocational impact analysis computed the adoption impact of 40 NGVs operating in California across ten different vocation types. The proposed evaluation framework included life-cycle nitrogen oxides (NOx) and carbon dioxide (CO2) emissions of natural gas, renewable natural gas and diesel fuel pathways and compared the lifetime NOx emission reduction potential of the considered vocation type vehicles. The resulting emission benefits of the fuel pathways were used to determine the most incentive-effective vocation types among the considered NGV applications. The multi-criteria decision-making analysis prioritized the fuel pathways based on multiple criteria which are related to an incentive effectiveness index as well as life cycle emissions. Refuse truck and transit bus pathways are likely to achieve the highest return for the total incentive granted when the vehicles are renewable natural gas (RNG)-powered. For compressed natural gas (CNG) fuel pathways, school and transit buses take the highest ranks over the various analysis scenarios. Each vocation type showed different incentive effects and emission reduction potential, which means that some vocational vehicles can play a critical role in the state’s funding and emission reduction plans. The suggested decision-making tool and assessment framework can provide useful reference data to improve the performance of future alternative fuel vehicle incentive programs.
Airports are a source of greenhouse gases (GHG) and air pollutants such as fine particulate matter with an aerodynamic diameter under 2.5 μm (PM2.5), which adversely affect the climate and human health. This pollution is worsening with increasing aircraft congestion. Even though aviation is the second largest source of GHG emissions in the transportation sector, it was excluded from the recent COP21 Paris Agreement. Little is known about the climate change and adverse health impacts from increasing airports congestion. The purpose of this study is to start filling this gap.
In this dissertation, I estimate congestion, health, and climate benefits from airport congestion pricing for Los Angeles International Airport (LAX), the fourth busiest airport in the world by passenger numbers in 2018. I first derive the optimal congestion fee for airports like LAX that primarily serve local and regional markets. To quantify the impacts of airport congestion pricing, I analyze one year of airport operations (2014), which corresponds to 593,547 flights (both inbound and outbound). My simulation results suggest that hourly congestion pricing would on average reduce waiting time by 2.9 minutes per flight and annual PM2.5 emissions by 11.4 percent, thus decreasing the environmental impacts from aircraft landing and takeoff operations (LTO), which extend as far as 19 km downwind from the airport.
An analysis of the health gains from implementing a congestion fee that accounts for air pollution cost shows that it would annually reduce premature mortality from PM2.5 exposure by 4.6 cases, avoided hospital admissions for cardiovascular diseases by 167 cases, and avoid 8,539 lost work days. The corresponding monetary value of these health gains are $45.8 million, $21.9 million, and $1.4 million respectively (all in 2014 dollars). For my climate change analysis, I consider both the country-level social cost of carbon (CSCC; $36 per tonne) and the global social cost of carbon (GSCC; $417 per tonne). While pricing GHG emissions with the CSCC only has a minor impact, using the GSCC helps further reduce aircraft congestion and its associated health impacts. Indeed, an aircraft congestion fee with GHG based on the GSCC would reduce premature
mortality by 6 cases each year, avoid hospital admissions by 221 cases, and avoid 11,528 lost work days (95% CI: 4,995, 18,060). The corresponding monetary value of these health gains are $60.7 million, $27.7 million, and $1.9 million respectively.
The methodology presented in this study is widely applicable. It provides engineers, planners, and policymakers a tool for reducing airport congestion and for quantifying the resulting health and climate benefits.
The ability to model the disruptions of adverse events on various systems, such as infrastructural and social, is an important tool to assessing these systems’ resilience. While previous research on system resilience concentrated on physical infrastructure such as transportation systems, two recent research topics include social resilience and dependencies across many infrastructure systems. For example, transportation is dependent on such systems as power, communications, and the workforces that are key to restoring these infrastructure systems. This dissertation contains three disruption modeling studies that have followed the evolution of resilience research over the past decade from physical systems to interrelated topics. The first study evaluates seismic risk of potential travel time increases from earthquake damage to bridges in a roadway network using mesoscopic traffic simulation. This analysis successfully obtained system risk curves of network-wide travel time increases. The second study shifts focus towards workforces that participate in restoring infrastructure systems. It identifies transportation and communications workers and calculates these workers’ exposure to the Peak Ground Accelerations (PGAs) of a 7.8 magnitude Southern California scenario earthquake. Indeed, for this scenario, transportation workers are exposed to statistically significant higher PGAs than non-transportation workers, and communication workers to significantly lower PGAs. The third study proposes a model for the travel time of information along communication-equipped vehicles physically traveling in a network. Vehicles are sampled as equipped vehicles, then their trajectories are analyzed to (1) estimate equipped vehicle link flow and turning movement counts and (2) estimate the frequency of equipped vehicles encountering each other on links and at nodes. This study compares two scenarios: the baseline scenario and a work zone scenario that corresponds to a bridge being damaged in the network. Preliminary results suggest a difference in expected path travel times when (1) the representation of a specified subpath within the sample is increased and (2) when vehicles are routed along currently unused subpaths. This dissertation concludes with a discussion of the implications of all three studies.
When Transportation Network Company (TNC) services first emerged, there was extensive discussion in the popular press and among academics about the benefits that these “shared” services would bring. TNC as a form of ground transportation to and from the airport in contrast, is less often studied or permitted. At Los Angeles International Airport for example, Uber and Lyft could not conduct pick-ups until about seven years after they were founded. TNC operations at airports are highly controversial, yet research on both airports and TNC’s rarely intersect. This dissertation aims to fill the gap in the literature and address such questions as: which and how many airports have various types of TNC service (standard, pooled)? How do they impact other modes, vehicle-occupancy, congestion, and access at airports? Can their service be modified (i.e. through pricing or service improvement) to encourage higher uses of shared modes? Using Uber and Lyft websites, it documents all airports in the U.S. and internationally that permit TNC service and the types of services available. It analyzes airport passenger surveys to evaluate how much TNC replaces and complements transit and the net effects at several airports. Also using the passenger survey, Google Maps Directions API, and other sources, it estimates travel time and costs of the different modes to the airport, builds a discrete choice model of the access mode choices, and simulates various scenarios. Some of the scenarios are a TNC price increase (to match the cost of taxis) or a price cut and travel time increase (to mimic Uber Pool and Lyft line which are carpool versions of TNC’s). Finally, it assesses how a pooled TNC service to the airport would operate. We apply the pick-up and delivery problem to airport access requests (formed based on the airport passenger survey) and measure the number of private trips that would be eliminated when passengers are pooled. The motivation for understanding the consequences of making private TNC’s more expensive, or pooled TNC’s less expensive and more efficient (with shorter detours or travel time) is to identify effective tools to encourage modal shifts to vehicles with higher occupancy.
Experience of daily commuters shows that stationary traffic patterns can be observed during peak periods in urban freeway networks. Such stationary states play an important role in many traffic flow studies. Theoretically, studies on the impact of capacity drop and design of traffic control strategies have been built on the assumption of stationarity. Mathematically, the existence and stability of stationary road networks have been proved within the framework of kinematic wave theories. Empirically, near-stationary states have been utilized for calibration of fundamental diagrams and investigation of traffic features at freeway bottlenecks. Therefore, an imperative need for real-world near-stationary data has been recognized to better understand and explore such above studies. However, there lacks an efficient method to identify near-stationary states.
In this research, an automatic method has been developed to efficiently identify near-stationary states from large amounts of inductive loop-detector data to fill the gap. The method consists of four steps: first, a data pre-processing technique is performed to select healthy datasets with sufficient congestion periods and normalize vehicle counts and occupancies to the same scale; second, a PELT changepoint detection method is applied to partitioning time series into candidate intervals; third, informative characteristics of each candidate, including duration and gap, are calculated; finally, near-stationary states are selected from candidates based on two well-designed selection criteria.
To calibrate two critical parameters of the method, a multi-objective optimization problem is formulated to consider the quantity and quality of near-stationary states as objective functions. Then a game theory approach is designed to convert the problem into a non-cooperative game. Further a game theory search algorithm with a built-in modified hill-climbing technique is developed to solve the game and obtain a unique Nash equilibrium solution. In an extended paradigm, a five-player game is built to achieve better performance on the near-stationary flow-occupancy pattern in the congested regime.
In an application, a calibration method of multi-lane multi-class fundamental diagrams with unifiable and non-FIFO properties is performed using identified near-stationary states. Results show that the calibrated multi-lane multi-class fundamental diagrams are well-fitted, physically meaningful, and have robust performance on the estimation and prediction of commodity flow-rates.
An unfortunate feature of freeway traffic flow at merge bottlenecks is the capacity drop (CD) phenomenon. It refers to a drop in the bottleneck outflow when a queue forms upstream to that bottleneck compared to the outflow observed before the formation of the queue. While its causes and exact mechanism are still open questions, this research concerns in the impacts of CD and how to mitigate them.
The distinct features of CD in a freeway corridor are assessed based on the behavior of equilibrium states in a model capable of replicating CD. The impacts are unveiled by comparing the system properties with and without the CD. The main finding is that the highest outflow occurs under uncongested equilibrium; however, it may not be reachable depending on the demands and initial conditions.
The local ramp metering control is investigated into more details. CD imposes a hysteresis on the system response with respect to the demand level. Also, we analyze the system in closed loop considering ALINEA, a well-known control algorithm. We establish the stability range with respect to parameters which is a necessary requirement for the controller to be effective. Further, we propose an extension of ALINEA to enlarge the stability range mitigating a performance loss that occurs when the on-ramp and the bottleneck are far apart.
Essential aspects of ramp metering are better captured with microscopic models; however, there were few evidences that such models can replicates CD. To that end, we propose a parameter calibration procedure that ensures the underlying model properly captures CD. The approach is tested with loop detector data from a merge bottleneck in which the CD is consistently observed. Finally, the analytical results are validated, and further practical aspects, such as detector placement and the effect of the controller sample time, are studied.
All results with different approaches point to the direction that the existence of CD imposes additional challenges on the system control. Fortunately, in most cases the effects of CD can be mitigated with a properly designed control strategy, such as the ones tested and proposed in this research.