Modeling capacity is an integral component towards traffic engineering objectives such as design of control strategies and evaluation of roadway improvement projects. Traffic dynamics at bottlenecks, both on freeways and on arterial networks, influenced by bounded acceleration and lane-changing, affect the capacity in intriguing ways. This research attempts to capture these impacts of the bounded acceleration behavior along with its interplay with lane-changing by constructing a modeling framework that accurately models traffic dynamics at bottlenecks.
First a modified Cell Transmission Model (CTM) is proposed, by substituting the traditionally constant demand function with a linearly decreasing function for congested traffic. Analytically the new model is shown to reproduce observed features in the discharge flow-rate and headway at signalized intersections. Calibration with headway observations further suggests that the model can reasonably capture all traffic queue discharge features. The solutions to the Riemann problem demonstrate that the modified CTM produces realistic results.
As a next step, the demand function is further modified by integrating macroscopic lane-changing effects on capacity, and a corresponding CTM, termed LCBA-CTM, is developed. The Lane Changing Bounded Acceleration CTM (LCBA-CTM) is shown to realistically model the capacity drop phenomenon and predict its magnitude at active freeway lane-drop bottlenecks in stationary states. Constant loading problems are analytically solved to reveal the onset and offset processes of capacity drop.
An addition to the framework connects microscopic acceleration profiles of vehicles to modified demand functions. This completes the framework presented by offering a mechanism to start with any acceleration model.
Finally, two applications of the modified CTM are presented. First, the framework is used to evaluate impacts of technological improvements such as autonomous vehicles on traffic dynamics at signalized intersections. In the second, the framework is used to create accurate Macroscopic Fundamental Diagrams for arterial networks.
This dissertation offers a systematic approach to incorporating bounded acceleration and lane-changing into the CTM demand functions. Such an approach is shown to capture important static and dynamic features at critical bottlenecks, including lost time and queue discharge features at signalized intersections, and capacity drop magnitude and the onset and offset of capacity drop at active freeway bottlenecks. Due to its ability to model impacts of bounded acceleration and lane-changing on macroscopic traffic statics and dynamics, this new model can be applied to design and evaluate new control and management strategies.
During the last two decades, a large body of empirical research has focused on the relationship between land use and travel behavior, and also on the impacts of transportation accessibility on land value. However, significant gaps remain in our understanding of these relationships. In this dissertation, I present three essays on accessibility, carless households, and long-distance travel that will enhance our understandings of the relationships between land use, land value, and transportation.
In my first essay, I provide empirical evidence about the magnitude of the value of transportation accessibility as reflected by residential rents in Rajshahi City, Bangladesh. Results of my SARAR (spatial autoregressive model with spatial-autoregressive disturbances) model show a small but statistically significant capitalization of accessibility. Results of this study should be useful for planning transportation infrastructure funding measures in least developed country cities like Rajshahi City.
In my second essay, I assess the joint effects of various socio-economic, residential, and land use variables on the likelihood that a household is carless, voluntarily or not, by analyzing data from the 2012 California Household Travel Survey (CHTS). Results of my binary logit models show the importance of land use diversity and of good transit service to help households voluntarily forgo their vehicles, and downplay the impact of population density and pedestrian-friendly facilities. Results of this study should help planners and policy makers formulate policies to curb automobile dependency and help promote sustainable urban transportation.
My third essay analyzes long-distance data from the 2012 CHTS to understand the influence of different socio-economic, land use, and land value variables on the likelihood that a household commutes long-distance in California. Results of my Generalized Structural Equation Model (GSEM) show that long-distance commuting is negatively associated with mixed density and residential home values (around commuters’ residences), but positively related with households’ car-ownership. My results also confirm the presence of residential self-selection. The empirical evidence of this study should help formulate land use planning strategies to curb long-distance commuting and thus help reducing vehicle-miles traveled, which is one way of reducing the emission of greenhouse gases from transportation.
The purpose of this study is to develop a traffic estimation framework which combines different data sources to better reconstruct the traffic states on the freeways. The framework combines both traffic parameter and state estimation in the same work flow, which resolves the inconsistency issue of most existing traffic state estimation methods.
To examine the quality of the traffic sensor data, the study starts with proposing the network sensor health problem (NSHP). The optimal set of sensors is selected from all sensors such that the violation of flow conservation is minimized. The health index for individual detector is then calculated based on the solutions. We also developed a tailored greedy search algorithm to find the solutions effectively. The proposed method is tested using the loop detector data from PeMS on a stretch of the SR-91 freeway. We compared the results with PeMS health status and found considerable level of consistency.
Two different traffic state estimation methods are proposed based on the data availability and traffic states. The LoopReid method is derived from the Newell’s simplified kinematic wave model by assuming the whole road segment is fully congested. We formulate a least square optimization problem to find the initial states and traffic parameters based on the first-in-first-out principle and the congested part of the Newell’s model. While developing the LoopCT method, we derived a counterpart of the Newell’s kinematic wave model in the Lagrangian coordinates under Eulerian boundary conditions. This model also leads to a new method to estimate vehicle trajectories within a road segment. We formulate a least square optimization problem in initial states and traffic parameters which works for mixed traffic states. The two estimation methods turned out to be highly related and the LoopCT method degenerates to the LoopReid method when the traffic is fully congested. The two methods are validated using two datasets from the NGSIM project. Both methods achieved considerable level of accuracy at reconstructing the traffic states and parameters.
Shared-use mobility systems, which enable users to have short-term access to transportation modes on an on-demand basis, have experienced tremendous growth over the last decade. However, most of the existing systems suffer from two co-founding issues: the lack of modeling tools to understand, simulate and predict their behavior and the lack of integration with the existing transit network. To address those issues, this dissertation focuses on investigating the operational challenges of bikesharing systems, with an emphasis on the rebalancing operations and the modeling of a new mobility concept, Car2work, which builds upon existing carsharing ideas and successfully integrates with existing transit networks. A methodological framework to solve the bikesharing rebalancing problem is proposed. The novelties of the approach are that it is proactive instead of reactive, as the bike redistribution occurs before inefficiencies are observed, and uses the outputs of a demand-forecasting technique to decompose the inventory and the routing problem. The decomposition makes the problem scalable, responsive to operator inputs, and able to accommodate user-specific models. Simulation results based on data from the Hubway bikesharing system show that system performance improvements of 7% in the afternoon peak could be achieved.
Car2work main goal is to connect commuters with workplaces while leveraging the line-haul capabilities of existing public transit systems and guaranteeing a trip back home, efficiently tackling the “last mile” problem that is a limiting characteristic of public transit. It differs from the traditional dynamic-ridesharing approaches because it is designed for recurrent commuting trips where commuters announce their (multiple) trips in advanced and an automated all-or-nothing matching strategy is performed, guaranteeing a ride home. The problem is formulated as a pure binary problem that is solved using an aggregation/disaggregation algorithm that renders optimal solutions. The solution approach is based on decomposing the problem into a master problem and a sub-problem, reducing the number of decision variables and constraints. As a result, various instances of the problem can be solved in reasonable amount of time, even when considering the transit network. The model could be used to simulate a large-scale implementation of the concept.
Optimization-based approaches are presented for the design of environmentally oriented road pricing and traffic rationing schemes, particularly with the objective of curbing human exposure to motor vehicle generated air pollutants. The focus on human exposure to pollutants advances previous road pricing and traffic rationing problems which primarily account for congestion minimization, emission minimization, or emissions constraints. Practical utilization of the proposed problems is hindered by their time-consuming nature, so surrogate-based algorithms are developed to accelerate the search for good problem solutions. Given that the algorithms are derivative-free, they can be applied to various types of computationally expensive transportation network design problems.
A toll design problem is proposed for selecting tolling locations and levels that minimize environmental inequality and human exposure to pollutants. A mixed-integer variant of the metric stochastic response surface algorithm and a hybrid genetic algorithm-metric stochastic heuristic are presented to solve the mixed integer toll design problem. Numerical tests suggest that the surrogate-based algorithms have superior performance relative to previous genetic algorithm-based methods.
In addition, an optimization problem is presented for the design of cordon and area-based road pricing schemes subject to environmental constraints. Flexible problem formulations are considered which can be easily utilized with state-of-the-practice transportation planning models. A surrogate-based solution algorithm that uses a geometric representation of the charging area boundary is proposed to solve cordon and area pricing problems.
Lastly, a bi-objective traffic rationing problem is considered where the planner attempts to maximize auto usage while minimizing pollutant exposure inequality, subject to constraints on the levels of greenhouse gas emissions and pollutant concentration levels. A personal pollutant exposure methodology is integrated with standard models used in transportation planning to simulate person-level pollutant intake. To solve this problem a surrogate-assisted differential evolution algorithm for multiobjective continuous optimization problems with constraints is proposed. A sample application illustrates a possible implementation of the traffic rationing problem and the ability of the proposed algorithm to find diverse feasible solutions
Since HFCVs are not yet in the market, there is not enough personal travel data with HFCVs to accurately estimate potential demand. Yet, for fuel companies, sufficient numbers of HFCVs are required before investment in more stations becomes profitable. Alternatively, for customers, sufficient numbers of stations are required before purchasing and operating HFCVs becomes a realistic alternative to ICEVs. So, the initial balancing between this supply and demand confliction is vital to the fate of HFCVs as a market force. This work investigates the effect of refueling availability on choosing HFCVs by finding saturation densities of refueling stations for these vehicles. Using a subsample of households in the NHTS 2009 dataset, we first use parameters of the utility of choosing Toyota Prius vs. Toyota Corolla. We then argue that the values of these coefficients can be transferred to AFVs in general, and used in a preference model for AFVs vs. ICEVs, provided that we also transfer the coefficients of the appropriate purchase and operating costs. Using these models as base, we express the operating costs of AFV with respect to the density of refueling stations and the mean value of time, which then are included in the logit model as variables. We then employ a dynamic normative model that accommodates both the “bandwagon” effect and the results of the estimation of the random utility model of choice to estimate proportions of AFVs in the market over time. Stabilized market proportions are then used for finding saturation densities of stations.
Then, using these results, a competition model is proposed to forecast supplies for HFCVs based on demands forecasted by the dynamic normative model. Feedback models are used connect results derived from the competition and dynamic normative models.
In this research, we propose a series of models designed to take advantage of availability of data—both structured and unstructured—from a variety of sources ranging from passive data, to questionnaires, to social media to analyze underlying patterns and trends of travel and activity behavior. The results support enhancements both in transportation planning and also in the application of programming to support such efforts.
First, a framework for automatically inferring the travel modes and trip purposes of human movement, when tracked by a GPS device, is introduced. We utilize a multiple changepoints algorithm to divide trajectories into segments using only speed data, with no use of referencing information or assumptions about the participants’ temporal or location contexts. Then, Random Forest is used to classify segments into moving and not—moving types. For moving segments, travel mode is predicted. Next, multiple machine learning algorithms are employed, validated, and tested to identify the most suitable model for inferring trip purposes. Estimation results indicate that Random Forest provides the best results. The overall prediction accuracy is over 80% on the testing set—both with and without data on socio-demographic variables—predicting “shop” trips with an accuracy of 92.1%, while its accuracy for “go home” and “studying” trips reaches 100%.
Additionally, we analyze data pertaining to responses to the introduction of light rail service taken in waves to complement and evaluate knowledge about how personal travel behavior varies over time of day, day of week, and between waves. Our results indicate that, although the average of activity duration varies significantly over days of week and waves, the random effect of these two factors on activity duration was minor; time of day contributed over one third of the total variance in the duration.
Finally, the dissertation demonstrates uses of Twitter data as a potentially important data source to understand comments, criticisms, and responses about light rail in Los Angles. This result can be useful for exploring trends among commuters and how their emotions varied according to the light rail line they used, the time of day, and the day of the week.
A large and growing body of literature associates proximity to major roadways with increased risk of many negative health outcomes and suggests that exposure to fine particulate matter may be a substantial factor. Directly emitted and non-reactive mobile source air pollutants such as directly emitted fine particulate matter can form large spatial concentration gradients along major roadways, in addition to causing significantly large temporal and seasonal variation in air pollutant concentrations within urban areas. Current modeling and regulatory approaches for minimizing exposure have limited spatial resolution and do not fully exploit the available data.
The objective is to establish a methodology for quantifying fine particulate matter concentration gradients due to mobile source pollutants and to estimate the resulting population exposure at a regional scale. A novel air dispersion modeling framework is proposed using EPA’s AERMOD with data from a regional travel demand model that can produce a high resolution concentration surface for a considerably large metropolitan area; in our case, Los Angeles County, California. We find that PM2.5 concentrations are highest and most widespread during the morning and evening commutes, particularly during the winter months. This is likely caused by a combination of stable atmospheric conditions during the early morning and after sunset in the evening and higher traffic volumes during the morning and evening commutes. During the midday hours concentrations are at their lowest even though traffic volumes are still much higher than during the evening. This is likely the result of heating during the day time which leads to unstable atmospheric conditions that cause more vertical mixing and lateral dispersion, reducing ground level PM2.5 concentrations by transport and dilution. With respect to roadway centerlines, PM2.5 concentrations drop off quickly, reaching relatively low concentrations between 150m to 200m from the center line of high volume roads. However, during stable atmospheric conditions (e.g., nighttime & winter season) concentrations remain elevated at distances up to 1,000m from roadway centerlines.
We will demonstrate the feasibility of our methodology and how integrating the dispersion modeling framework into the travel demand modeling process routinely performed when developing and analyzing regional transportation improvement initiatives can lead to more environmentally and financially sustainable transportation plans. Regional strategies that minimize exposure, rather than inventories, could be established, environmental justice concerns are easily identified, and projects likely to cause local pollution “hotspots” can be proactively screened out, saving time and money for the transportation agency.
A macroscopic relation between the network-level average flow-rate and density, which is known as the macroscopic fundamental diagram (MFD), has been shown to exist in urban networks in stationary states. In the literature, however, most existing studies have considered the MFD as a phenomenon of urban networks, and few have tried to derive it analytically from signal settings, route choice behaviors, or demand patterns. Furthermore, it is still not clear about the definition or existence of stationary traffic states in urban networks and their stability properties. This dissertation research aims to fill this gap.
I start to study the stationary traffic states in a signalized double-ring network. A kinematic wave approach is used to formulate the traffic dynamics, and periodic traffic patterns are found using simulations and defined as stationary states. Furthermore, traffic dynamics are aggregated at the link level using the link queue model, and a Poincar ́ map approach is introduced to analytically define and solve possible stationary states. Further results show that a stationary state can be Lyapunov stable, asymptotically stable, and unstable. Moreover, MFD is explicitly derived such that the network flow-rate is a function of the network density, signal settings, and route choice behaviors. Also the time for the network to be gridlocked is analytically derived.
Even with the link queue model, traffic dynamics are still difficult to solve due the discrete control at signalized junctions. Therefore, efforts are also devoted to deriving invariant continuous approximate models for a signalized road link and analyzing their properties under different capacity constraints, traffic conditions, traffic flow fundamental diagrams, signal settings, and traffic flow models. Analytical and simulation results show that the derived invariant continuous approximate model can fully capture the capacity constraints at the signalized junction and is a good approximation to the discrete signal control under different traffic conditions and traffic flow fundamental diagrams. Further analysis shows that non-invariant continuous approximate models cannot be used in the link transmission model since they can yield no solution to the traffic statics problem under certain traffic conditions.
For a signalized grid network, simulations with the link queue model confirm that important insights obtained for double-ring networks indeed apply to more general networks.
Transportation agencies tasked with forecasting freight movements, creating and evaluating policy to mitigate transportation impacts on infrastructure and air quality, and furnishing the data necessary for performance driven investment depend on quality, detailed, and ubiquitous vehicle data. Unfortunately, commercial vehicle data is either missing or expensive to obtain from current resources. To overcome the drawbacks of existing commercial vehicle data collection tools and leverage the already heavy investments into existing sensor systems, we present a novel approach of integrating two existing data collection devices to gather high resolution truck data – Weigh-in-motion (WIM) systems and advanced inductive loop detectors (ILD). Each source provides a unique data set that when combined produces a synergistic data source that is particularly useful for truck body class modeling. Since body configuration is closely linked to commodity carried, drive and duty cycle, and other operating characteristics, it is inherently useful for each of the above mentioned applications.
In this work we describe the physical integration including hardware and data collection procedures undertaken to develop a series of truck body class models. Approximately 33,000 samples consisting of photo, WIM, and ILD signature data were collected and processed representing a significant achievement over previous ILD signature models which were limited to around 1,000 commercial vehicle records.
Three families of models were developed, each depicting an increasing level of input data and output class resolution. The first uses WIM data to estimate body class volumes of five semi-trailer body types and individual predictions of two tractor body classes for vehicles with five axle tractor trailer configurations. The trailer model produces volume errors of less than 10% while the tractor model resulted in a correct classification rate (CCR) of 92.7%. The second model uses ILD signatures to predict 47 vehicle body classes using a multiple classifier system (MCS) approach coupled with the Synthetic Minority Oversampling Technique (SMOTE) for preprocessing the training data samples. Tests show the model achieved CCR higher than 70% for 34 of the body classes. The third and most complex model combines WIM and ILD signatures using to produce 63 body class designations, 52 with CCR greater than 70%. To highlight the contributions of this work, several applications using body class data derived from the third model are presented including a time of day analysis, average payload estimation, and gross vehicle weight distribution estimation.