Enhanced Methods for Utilization of Data to Support Multi-Scenario Analysis and Multi-Resolution Modeling

The success of analysis and simulation in transportation systems depends on the availability, quality, reliability, and consistency of real-world data and the methods for utilizing the data. Additional data and data requirements are needed to support advanced analysis and simulation strategies such as multi-resolution modeling (MRM) and multi-scenario analysis. This study has developed, demonstrated, and assessed a systematic approach for the use of data to support MRM and multi-scenario analysis. First, the study developed and examined approaches for selecting one or more representative days for the analysis, considering the variability in travel conditions throughout the year based on cluster analysis. Second, this study developed and analyzed methods for using crowdsourced data vii to estimate origin-destination demands and link-level volumes for use as part of an MRM with consideration of the modeling scenario(s). The assessment of the methods to select the representative day(s) utilizes statistical measures, in addition to measures and visualization techniques that are specific to traffic operations. The results of the assessment indicate that the utilization of the K-means clustering algorithm with four clusters and spatio-temporal segregation of the variables demonstrated superior performance over other tested approaches, such as the use of the Gaussian Mixture clustering algorithm and the use of different segregation levels. The study assessed methods for the use of third-party crowdsourced data from StreetLight (SL) as part of the Origin-Destination Matrix Estimation (ODME), which identifies the method resulting in the closest origin-destination demands to the original seed matrices and real-world link counts. The results of the study indicate that Method 3(b) produced the best performance, which utilized combined data from demand forecasting models, crowdsourced data, and traffic counts. Additionally, this study examined regression models between crowdsourced data and count station data developed for link-level estimation of the volumes. This study also examined the accuracy and transferability of the link-level estimation of the volumes to determine if the crowdsourced data combined with available volume data at several locations can be used to predict missing or unavailable volumes in different locations on different days and times within the network. Regression models produced low errors than the default SL estimates when hourly or daily traffic volumes were taken into account. For similar traffic conditions, the models predicted directional traffic volume close to the real-world value

A REAL-TIME TRAFFIC CONDITION ASSESSMENT AND PREDICTION FRAMEWORK USING VEHICLE-INFRASTRUCTURE INTEGRATION (VII) WITH COMPUTATIONAL INTELLIGENCE

This research developed a real-time traffic condition assessment and prediction framework using Vehicle-Infrastructure Integration (VII) with computational intelligence to improve the existing traffic surveillance system. Due to the prohibited expenses and complexity involved for the field experiment of such a system, this study adopted state-of-the-art simulation tools as an efficient alternative. This work developed an integrated traffic and communication simulation platform to facilitate the design and evaluation of a wide range of online traffic surveillance and management system in both traffic and communication domain. Using the integrated simulator, the author evaluated the performance of different combination of communication medium and architecture. This evaluation led to the development of a hybrid VII framework exemplified by hierarchical architecture, which is expected to eliminate single point failures, enhance scalability and easy integration of control functions for traffic condition assessment and prediction. In the proposed VII framework, the vehicle on-board equipments and roadside units (RSUs) work collaboratively, based on an intelligent paradigm known as \u27Support Vector Machine (SVM),\u27 to determine the occurrence and characteristics of an incident with the kinetics data generated by vehicles. In addition to incident detection, this research also integrated the computational intelligence paradigm called \u27Support Vector Regression (SVR)\u27 within the hybrid VII framework for improving the travel time prediction capabilities, and supporting on-line leaning functions to improve its performance over time. Two simulation models that fully implemented the functionalities of real-time traffic surveillance were developed on calibrated and validated simulation network for study sites in Greenville and Spartanburg, South Carolina. The simulation models\u27 encouraging performance on traffic condition assessment and prediction justifies further research on field experiment of such a system to address various research issues in the areas covered by this work, such as availability and accuracy of vehicle kinetic and maneuver data, reliability of wireless communication, maintenance of RSUs and wireless repeaters. The impact of this research will provide a reliable alternative to traditional traffic sensors to assess and predict the condition of the transportation system. The integrated simulation methodology and open source software will provide a tool for design and evaluation of any real-time traffic surveillance and management systems. Additionally, the developed VII simulation models will be made available for use by future researchers and designers of other similar VII systems. Future implementation of the research in the private and public sector will result in new VII related equipment in vehicles, greater control of traffic loading, faster incident detection, improved safety, mitigated congestion, and reduced emissions and fuel consumption

Computationally efficient offline demand calibration algorithms for large-scale stochastic traffic simulation models

Thesis: Ph. D. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2018.Cataloged from PDF version of thesis.Includes bibliographical references (pages 168-181).This thesis introduces computationally efficient, robust, and scalable calibration algorithms for large-scale stochastic transportation simulators. Unlike a traditional "black-box" calibration algorithm, a macroscopic analytical network model is embedded through a metamodel simulation-based optimization (SO) framework. The computational efficiency is achieved through the analytical network model, which provides the algorithm with low-fidelity, analytical, differentiable, problem-specific structural information and can be efficiently evaluated. The thesis starts with the calibration of low-dimensional behavioral and supply parameters, it then addresses a challenging high-dimensional origin-destination (OD) demand matrix calibration problem, and finally enhances the OD demand calibration by taking advantage of additional high-resolution traffic data. The proposed general calibration framework is suitable to address a broad class of calibration problems and has the flexibility to be extended to incorporate emerging data sources. The proposed algorithms are first validated on synthetic networks and then tested through a case study of a large-scale real-world network with 24,335 links and 11,345 nodes in the metropolitan area of Berlin, Germany. Case studies indicate that the proposed calibration algorithms are computationally efficient, improve the quality of solutions, and are robust to both the initial conditions and to the stochasticity of the simulator, under a tight computational budget. Compared to a traditional "black-box" method, the proposed method improves the computational efficiency by an average of 30%, as measured by the total computational runtime, and simultaneously yields an average of 70% improvement in the quality of solutions, as measured by its objective function estimates, for the OD demand calibration. Moreover, the addition of intersection turning flows further enhances performance by improving the fit to field data by an average of 20% (resp. 14%), as measured by the root mean square normalized (RMSN) errors of traffic counts (resp. intersection turning flows).by Chao Zhang.Ph. D. in Transportatio

Evaluation of Safety and Mobility Benefits of Connected and Automated Vehicles by Considering V2X Technologies

The recent development in communication technologies facilitates the deployment of connected and automated vehicles (CAV) which are expected to change the future transportation system. CAV technologies enable vehicles to communicate with other vehicles through vehicle-to-vehicle (V2V) communications and the infrastructure through Vehicle-to-infrastructure (V2I) communications. Since the real-world CAV data is not currently available as of today, simulation is the most commonly used platform to evaluate the future V2X system. Although several studies evaluated the effectiveness of CAVs in a small roadway network, there is a lack of studies analyzing the impact of CAVs at the network level by considering both freeways and arterials. Also, none of the previous studies have attempted to differentiate the benefits of CAVs over only automated vehicles (AVs) by incorporating multiple preceding vehicles\u27 information (i.e., acceleration, position, etc.). On the other hand, most of the simulation-based studies assumed the uninterrupted communication between vehicles in the CAV environment which might not be feasible in reality. Hence, there is still a research gap that exists for which this study tried to fill this gap. Therefore, this study developed a calibrated and validated large-scale network for the deployment of CAV technologies by utilizing Dynamic Traffic Assignment (DTA) model in Orlando metropolitan area, Florida, using Multi-Resolution Modeling (MRM) technique. Also, the study proposed a signal control algorithm through V2I technology in order to elevate the performance of CAVs at intersections. Different car-following models were utilized to approximate different CAV technologies (CAV, AV, and CV (connected vehicle)) in the simulation environment. Hence, the study analyzed the benefits of CAV over AV with different market penetration rates (MPRs). Furthermore, the study considered the performance of different communication system along with the traffic condition by utilizing Dedicated Short-Range Communications (DSRC or IEEE 802.11p) and wireless access (IEEE 1609 protocol) for the application of vehicle ad-hoc network (VANET). To this end, the study evaluated the safety effectiveness of different communication protocols under the CAV environment. Aimsun Next and SUMO & OMNET++ based Veins simulator were used as the simulation platform. Different car-following models, signal control algorithm, and communication systems were coded by using the application programming interface (API) and C++ language. For the traffic efficiency, the study utilized travel time and travel time rate (TTR) while for the safety evaluation, different surrogate safety measures; speed, and crash-risk models were used. Also, several statistical tests (e.g., t-test, ANOVA) and modeling techniques (e.g., generalized estimating equation, logistic regression, etc.) were developed to analyze both safety and mobility. The results of this study implied that CAV could improve both safety and efficiency at the network level with different MPRs. Also, CAV is more efficient compared to the only AV in terms of both traffic safety and mobility. Different communication protocols have a significant effect on traffic safety under the CAV environment. Finally, the results of this study provide insight to transportation planners and the decision makers about the benefits of CAV at the network level, different CAV technologies, and the performance of different communication systems under the CAV environment

Walk-sharing - A smarter way to improve pedestrian safety and safety perception in urban spaces

Fortbewegung zu Fuß ist nachweislich der körperlichen und geistigen Gesundheit der Menschen zuträglich und gilt als Schlüssel zu nachhaltigem und lebenswertem städtischem Leben. Der Anteil der Fußgänger am Verkehrsaufkommen ist allerdings mit der rasanten Motorisierung und Verstädterung auf der ganzen Welt rückläufig. Darüber hinaus halten fußgängerunfreundliche Umgebungen Menschen davon ab, zu Fuß zu gehen. Die Angst vor Kriminalität wurde als wichtigstes Hindernis genannt. Sie macht das Zufußgehen zu kritischen Tageszeiten unattraktiv, selbst wenn es nach allen anderen Maßstäben bequem wäre. Die Furcht vor Kriminalität beeinflusst die Wahl des Weges und der Verkehrsmittel. Sie motiviert die Menschen dazu, kostspieligere Alternativen zu nutzen, zum Beispiel sinnvolle Umwege zu gehen oder ganz auf das Gehen zu verzichten und auf andere, meist motorisierte Verkehrsmittel umzusteigen. Die Angst vor Kriminalität verringert die allgemeine Begehbarkeit eines Stadtgebiets, reduziert die Zeit, die zu Fuß verbracht wird, und verhindert damit die Vorteile, die das Zufußgehen geboten hätte. Herkömmliche Ansätze zur Verringerung der Furcht vor Kriminalität in Außenbereichen umfassen städtebauliche Verbesserungen und Infrastrukturüberholungen. Sie sind teuer, lokal begrenzt und erfordern einen erheblichen Zeit- und Personalaufwand. Andere, neuere, ortsgestützte IT-Ansätze, die zum Beispiel sichere Routenempfehlungssysteme beinhalten, leiden unter einer starken Abhängigkeit von Kriminalitäts- und anderen Daten und sind dafür bekannt, dass sie Gesellschaften durch die Erstellung von Profilen sozioökonomischer Gruppen segregieren. Um die Herausforderungen der bestehenden Methoden zu überwinden, wird in dieser Arbeit das Walk-Sharing (wörtlich: gemeinsames Gehen) eingeführt. Walk-Sharing ist ein neuartiger Service in der Kategorie der geteilten Mobilität, die darauf abzielt, Menschen dazu zu ermutigen, zu Fuß zu gehen, anstatt andere Verkehrsmittel zu nutzen, wenn dies möglich ist. Da sich Menschen sicherer fühlen, wenn sie in Begleitung gehen, bringt Walk-Sharing Menschen mit ähnlichen räumlichen und zeitlichen Mobilitätsbedürfnissen zusammen, die bereit sind, zu Fuß zu ihren jeweiligen Zielen zu gehen. Durch das gemeinsame Gehen für einen Teil oder die gesamte Strecke verbessert das Walk-Sharing die aktive natürliche Wachsamkeit und verringert so die Angst vor Kriminalität. Durch die Verringerung der Angst vor Kriminalität während des Gehens hat Walk-Sharing das Potenzial, das Gehen attraktiver zu machen und damit den Anteil des Fußverkehrs auf kurzen Strecken zu erhöhen und folglich den motorisierten Verkehr zu reduzieren, was wiederum zu einer Verringerung der Emissionen und der Verkehrsbelastung führt. In dieser Arbeit werden die Grundlagen des Walk-Sharing erörtert, seine Gemeinsamkeiten und Unterschiede zu bestehenden geteilten Mobilitätsformen herausgearbeitet und ein konzeptionelles Modell vorgeschlagen, das eine abstrakte Darstellung eines möglichen Walk-Sharing-Systems darstellt. Basierend auf der Logik dieses konzeptionellen Modells wird in dieser Arbeit ein agentenbasiertes Simulationsmodell vorgestellt, um die Leistung von Walk-Sharing unter plausiblen Szenarien objektiv zu messen. Anhand theoretischer Simulationen wird das Sensitivitätsverhalten des Walk-Sharing-Modells dargestellt, was auch die logische Funktion des Modells selbst zeigt. Danach werden begründeter Annahmen über menschliche Präferenzen herangezogen, um eine Simulation des Walk-Sharing auf einem Universitätscampus vorzustellen. Diese Simulation zeigt bis zu 80% Effektivität in Bezug auf die Verbesserung der Sicherheit. Schließlich werden in dieser Arbeit eine Umfrage und deren Ergebnisse vorgestellt, die die tatsächlichen räumlich-zeitlichen Präferenzen, die sozialen Präferenzen und die allgemeine Wahrscheinlichkeit der Teilnahme an Walk-Sharing aufzeigen. Mit diesen Erkenntnissen wird eine kalibrierte, ausgefeiltere und fundiertere Simulation des Walk-Sharing vorgestellt. Die Ergebnisse zeigen, dass gemeinsames Gehen bis zu 60% zur Verbesserung der Sicherheit beiträgt und gleichzeitig räumlich-zeitliche Kosten verursacht, die im Rahmen der von der befragten Gruppe bevorzugten Standards liegen. Walk-Sharing überwindet die Nachteile der bestehenden Ansätze zur Verringerung der Kriminalitätsfurcht, indem es proaktiv (unabhängig von Kriminalitäts- und stellvertretenden soziodemographischen Daten) und kostengünstig ist (keine größeren infrastrukturellen Veränderungen oder erheblicher menschlicher Aufwand erforderlich). Es ist skalierbar und übertragbar (kann überall angewendet werden und ist für die Gesellschaft angesichts der gegenwärtigen Verbreitung von Smartphones leicht zugänglich). Im Zeitalter des ubiquitären Computings, des Internets der Dinge, effizienter standortbezogener Dienste, und Smartphones könnte Walk-Sharing die intelligentere Lösung sein, die das Zufußgehen als sicherere Mobilitätsform für räumlich und zeitlich günstige Wege fördert und somit Fortschritte in Richtung eines nachhaltigeren städtischen Lebens macht, indem sie die aktive Mobilität erhöht und den motorisierten Verkehr reduziert

Link dependent origin-destination matrix estimation : nonsmooth convex optimisation with Bluetooth-inferred trajectories

This thesis tackles the traditional transport engineering problem of urban traffic demand estimation by using Bluetooth data and advanced signal processing algorithms. It proposes a method to recover vehicles trajectories from Bluetooth detectors and combining vehicle trajectories with traditional traffic datasets, traffic is estimated at a city level using signal processing algorithms. Involving new technologies in traffic demand estimation gave an opportunity to rethink traditional approaches and to come up with new method to jointly estimate origin-destinations flows and route flows. The whole methodology has been applied and evaluated with real Brisbane traffic data

Proceedings, MSVSCC 2011

Proceedings of the 5th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 14, 2011 at VMASC in Suffolk, Virginia. 186 pp

Intelligent Transportation Related Complex Systems and Sensors

Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems. It includes twenty-four papers, which cover scientific concepts, frameworks, architectures and various other ideas on analytics, trends and applications of transportation-related data