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This thesis investigates two research problems in analyzing floating car data (FCD): automated segmentation and privacy. For the former, we design an automated segmentation method based on the social functions of an area to enhance existing traffic demand analysis. This segmentation is used to create an extension of the traditional origin-destination matrix that can represent origins of traffic demand. The methods are then combined for interactive visualization of traffic demand, using a floating car dataset from a ride-hailing application. For the latter, we investigate the properties in FCD that may lead to privacy leaks. We present an attack on a real-world taxi dataset, showing that FCD, even though anonymized, can potentially leak privacy. / Graduate

Traffic congestion has been placing an extremely high burden on the development of modern cities. Congestion can be alleviated by either increasing road capacity, or by reducing traffic demand. For decades, increasing capacity by building more roads and lanes has been the major solution applied to accommodate the ever-growing traffic demand. However, it turns out to be of limited effect due to some well-known phenomenon such as latent demand. Controlling and managing traffic demand has in turn been viewed as a cost-effective alternative to increasing road capacity, as has been demonstrated many successful applications all around the world. Within the concept framework of Traffic Demand Management (TDM), Active Transportation and Demand Management (ATDM) is the dynamic management, control, and influence of traffic demand and traffic flow of transportation facilities. ATDM strategies attempt to influence traveler behavior and further manage traffic flow in a time-dependent manner within the existing infrastructure Successful ATDM applications include congestion pricing, adaptive ramp metering, dynamic speed limits, dynamic lane use control, etc. Singapore stands out to be an excellent success story of ATDM, as the implementations of "Cap and Trade" license plates and electronic road pricing make motoring a high cost privilege for citizens of Singapore, making the public relies on transit. Monetary leverage is an effective instrument to facilitate ATDM. Examples of ATDM applications adopting monetary instrument includes dynamic congestion pricing, "Cap and Trade" of car licenses, etc. Taking congestion pricing as an example, policy makers are inducing travelers' behavior and alternating their preferences towards different behavior decisions by levying price tags to different choices. As an important underpinning of rationing choice theory, an individual assigns an ordinal number over the available actions and this ordinal number is calculated by their utility function or payoff function. The individual's preference is expressed as the relationship between those ordinal assignments. In the implementation of congestion pricing, policy makers are imposing an additional high disutility to congested roads and therefore pushing some of the travelers to take alternative routes or shift to alternative departure times or even cancel the trips. However, congestion pricing suffers from public aversion as it creates burden on the motoring of low-income people and therefore doesn't help to alleviate social inequality. The concept of Tradable Mobility Credit (TMC) has been proposed by a group of researchers as another innovative application to facilitate dynamic traffic demand management and solve social inequality issues using pricing instruments. The concept of TMC is borrowed from carbon trading in environmental control. A limited quota of personal auto usage is issued to eligible travelers and credits can be traded in a free market fashion. This guarantees that the roadway usage does not exceed capacity while avoiding the negative effects of shortages normally associated with quotation systems. TMC is literally not a market-ready policy as the integration of the supporting infrastructures, including the trading market, the credit assignment component, and the credit charging component, has not been fully explored yet. Existing TMC research focuses on explaining and exploring the equilibrium condition through analytical methods such as mathematical modeling. Analytical models produce perfect convergence curves and deterministic equilibrium traffic flow patterns. Analytical models provide influential guidance for further works but the solution procedure may encounter problems when dealing with larger real world networks and scenarios. Meantime, current analytical models don't consider the microstructure of the credit trading market sufficiently while it's actually the most unique component of TMC system. Motivated by those concerns, an integrated TMC evaluation platform consisting of a policy making module and traveler behavior modules are proposed in this research. The concept of Agent-Based Modeling and Simulation (ABMS) is extensively adopted in this integrated platform as each individual traveler carries his/her personal memory across iterations. The goal of establishing this framework is to better predict a traveler's route choice and trading behavior if TMC is imposed and further provide intelligence to potential policy makers' decision making process. The proposed integrated platform is able to generate results at different aggregation levels, including both individual level microscopic behavior data as well as aggregated traffic flow and market performance data. In order to calibrate the proposed integrated platform, an online interactive experiment is designed based on an experimental economic package and a human research element with 22 participants has been conducted on this experiment platform to gather field data regarding a real person's route choice behavior and credit trading behavior in an artificial TMC system. Participants are recruited from forum, listserve, social media, etc. The calibrated platform is proved to have the ability to predict travelers' behavior accurately. A prototype market microstructure is proposed in this research as well and it is proved to be a cost-effective setting and resulted to a vast amount of economic saving given the fact that travelers would behave similar to the prediction generated by traveler behavior module. It's also demonstrated that the principle of Pareto-improving is not achieved in the proposed ABMS models.

This thesis is submitted for the degree of Doctor of Engineering in Environmental Technology. The degree is awarded for industrially relevant research, based in industry, and supported by a programme of development courses. This project aims to contribute to a reduction in the environmental burdens of vertical transportation systems. The author has carried out an environmental assessment showing that the dominating environmental burdens of vertical transportation systems arise from their use of electricity while in operation in buildings. An assessment of traffic demand has concluded that we are probably over-sizing lifts, and are therefore installing systems that consume more energy than necessary. Traffic planning techniques for single and double deck lifts have been reviewed and developed. The kinematics (motion) of lifts has been studied. New formulae have been derived that allow us to plot travel profiles for any input of journey distance, maximum velocity, maximum acceleration and maximum jerk. Taking these journey profiles as inputs, a mathematical model of a DC Static Converter Drive has been developed. The model can be used to calculate the energy consumption of any individual lift trip. A lift simulation program has been developed. The program uses the research in traffic, kinematics and motor modelling as a basis for developing energy saving lift control strategies.

This paper presents a combined method for short-term forecasting of detector counts in urban networks and subsequent traffic demand estimation using the forecasted counts as constraints to estimate origin-destination (OD) flows, route and link volumes. The method is intended to be used in the framework of an adaptive traffic control strategy with consecutive optimization intervals of 15. min. The method continuously estimates the forthcoming traffic demand that can be used as input data for the optimization. The forecasting uses current and reference space-time-patterns of detector counts. The reference patterns are derived from data collected in the past. The current pattern comprises all detector counts of the last four time intervals. A simple but effective pattern matching is used for forecasting. The subsequent demand estimation is based on the information minimization model that has been integrated into an iterative procedure with repeated traffic assignment and matrix estimation until a stable solution is found. Some enhancements including the improvement of constraints, redundancy elimination of these constraints and a travel time estimation based on a macroscopic simulation using the Cell Transmission Model have been implemented. The overall method, its modules and its performance, which has been assessed using artificially created data for a real sub-network in Hannover, Germany, by means of a microsimulation with Aimsun NG, are presented in this paper.

There are experimental based software packages as well as traffic simulation models that are used for analyzing life cycle cost of road projects. Among those our study was focused on currently available models to analyze the road user delay costs and to identify factors affecting road user delay costs. Sensitivity analyses were performed to identify the important factors that influence the user delay cost. Finally, prediction of future traffic demand as well as user delay cost, using the binomial lattice model, were presented to include the uncertainty of future traffic and user delay costs. The results of this study could help the highway designers with evaluating the future traffic.

Departments of Transportation (DOTs) are responsible for keeping their road network in a state of good repair while also aiming to reduce congestion through the implementation of different traffic control and demand management strategies. These strategies can result in changes in traffic volume distributions, which in turn affect the level of pavement deterioration due to traffic loading. To address this issue, this dissertation introduces an integrated simulation-optimization framework that accounts for the combined effects of pavement conditions and traffic management decision-making strategies. The research focuses on exploring the range of possible performance outcomes resulting from this integrated modeling approach. The research also applied the developed framework to a particular traffic demand management strategy and assessed the impact of dynamic tolls around the specific site of I-66 inside the beltway. The integrated traffic-management/pavement-treatment framework was applied to address both the operational and pavement performance of the network. Aimsun hybrid macro/meso dynamic user equilibrium experiments were used to simulate the network with a modified cost function taking care of the dynamic pricing along the I-66 tolled facility. Furthermore, the framework was expanded to include the development of a systematic and comprehensive methodology to optimize the allocation of networkwide pavement treatment work zones over space and time. The proposed methodology also contributed to the development of a surrogate function that reduces the optimization computation burden so that researchers would be able to conduct work zone allocation optimization without having to run expensive simulation work. Finally, in this dissertation, a user-friendly decision-support tool was developed to assist in the pavement treatment and project selection planning process. We use machine learning models to encapsulate the simulation optimization process. / Doctor of Philosophy / Departments of Transportation (DOTs) are responsible for keeping their road network in a state of good repair. Improvement in pavement rehabilitation plans can lead to savings in the order of tens of millions of dollars. Pavement rehabilitation plans result in work zone schedules on the transportation network. Limited roadway capacities due to work zones affect traffic assignments and routing on the roads, which impacts the selection of optimal operation strategies to manage the resulting traffic. On the other hand, the choice of any particular operation and routing strategy will result in different distributions of traffic volumes on the roads and affect the pavement deterioration levels due to traffic loading, leading to other optimal rehabilitation plans and corresponding work zones. While there have been several research efforts on infrastructure condition assessment and other research efforts on traffic control and demand management strategies, there is a wide gap in the nexus of the two fields. To address this issue, this dissertation introduces an integrated simulation-optimization framework that accounts for the combined effects of pavement conditions and traffic management decision-making strategies. The research focuses on exploring the range of possible performance outcomes resulting from this integrated modeling approach. The research also applied the developed framework to a particular traffic demand management strategy and assessed the impact of dynamic tolls around the specific site of I-66 inside the beltway. The integrated traffic-management/pavement-treatment framework was applied to address both the operational and pavement performance of the network. Furthermore, the framework was expanded to include the development of a systematic and comprehensive methodology to optimize the allocation of networkwide pavement treatment work zones over space and time. The proposed methodology also contributed to the development of a surrogate function that reduces the optimization computation burden so that researchers would be able to conduct work zone allocation optimization without having to run expensive simulation work. Finally, in this dissertation, a user-friendly decision-support tool was developed to assist in the pavement treatment and project selection planning process. We use machine learning models to encapsulate the simulation optimization process.

The purpose of this study is to assist TxDOT engineers in the field of traffic control planning. This is to be done via the creation of a Geographic Information System (GIS) based tool. By bringing together information about TxDOT’s on-system roadways’ geographical locations, traffic demands, and capacities, one aggregate database has been established. Using the tools of GIS, Microsoft Excel, Microsoft Access, and VBA programming, a static clickable interface has been constructed. It enables users to access properties for any selected roadway link they desire. Expansion of the product to ArcIMS is ongoing to allow easy access for end users via the internet. / text

During the last century, the number of car users has increased as an effect of the increasing population growth. To manage the environmental and infrastructural challenges that comes with a more congested traffic network, traffic planning has become of higher importance to analyze the current traffic state and to predict future capacity challenges and effects of investments. These analysis and evaluations are commonly performed in different traffic analysis tools, where updated and realistic traffic demand needs to be provided to ensure reasonable results. In this thesis, a macroscopic model of Norrköping municipality constructed in the traffic demand modelling software Visum and a daily Origin-Destination(OD)-matrix is considered. The goal of this thesis is to produce a method that modify the current daily demand matrix into hourly demand matrices, called hourly target matrices, that represents a typical weekday. The goal is also to implement and evaluate the OD-estimation algorithm Simultaneous Perturbation Stochastic Approximation (SPSA) to obtain updated and valid demand matrices for the network model of Norrköping. The method of dividing the daily demand matrix into hourly target matrices is based on the paper by Spiess %26 Suter (1990). The method makes use of the available daily trip purpose matrices combined with hourly link flow observations from 96 links in a multiple linear regression model to obtain 24 hourly demand matrices. The resulting matrices are compared with the link flow observations and has different levels of R^2-fit, the maximum fit is 85.79 % and the minimum fit is 55.89 %. The average R^2-value is 72 %. The OD-estimation based on SPSA is performed on the AM and PM peak hours. The algorithm is implemented in Python scripts that are called from Visum where the traffic assignments is calculated. The result is an increase in R^2-value since the link flow difference between estimated and observed link flow is decreased. In total, the estimated link flows are improved by 7.4 % in the AM peak hour and 15.6 % in the PM peak hour. The total absolute change in OD-demand is 3 871 trips for AM peak hour and 6 452 trips for the PM peak hour. The estimated OD-matrices are evaluated by qualitatively visualizing the difference in heat maps and in the quantitative measure structural similarity index. The result is no major structural change from the hourly target matrices which verifies that the information used when the target matrices is produced still is considered. The total demand increased in both hours, with 505 respectively 2 431 trips and flows in some OD-pairs has a very high percental change. This was restricted by adding a penalty term to the SPSA-algorithm on the PM peak hour. The result of penalized SPSA is a much less increase of total demand as well as less percental change of the OD-flows. Though, this to a cost of not decreasing the link flow difference in the same magnitude.

The aim of this research study is to analyse the demand for public transport on the route Zliv-Ceske Budejovice. Its main aim is to figure out whether comuters prefer public transport to personal transport and what effect does that make on the environment. The analysis was based on findings both prior and posterior demand. The basis for the processing methodology and study of scientific literature is held with a focus on transport and related topics. An integral part of the methodology process is quality data obtained from residents and those moving to session Zliv-Ceske Budejovice. The method used, allows us to analyse the differences between real and desired transport demands. The results of this work will help us manage public transport efficiently, in order to increase the attractiveness of public transport in the session and as a consequence, the negative environmental impacts of other modes of transport.