Exploring the Potentials of Using Crowdsourced Waze Data in Traffic Management: Characteristics and Reliability

Real-time traffic information is essential to a variety of practical applications. To obtain traffic data, various traffic monitoring devices, such as loop detectors, infrastructure-mounted sensors, and cameras, have been installed on road networks. However, transportation agencies have sought alternative data sources to monitor traffic, due to the high installation and maintenance cost of conventional data collecting methods. Recently, crowdsourced traffic data has become available and is widely considered to have great potential in intelligent transportation systems. Waze is a crowdsourcing traffic application that enables users to share real-time traffic information. Waze data, including passively collected speed data and actively reported user reports, is valuable for traffic management but has not been explored or evaluated extensively. This dissertation evaluated and explored the potential of Waze data in traffic management from different perspectives. First, this dissertation evaluated and explored Waze traffic speed to understand the characteristics and reliability of Waze traffic speed data. Second, a calibration-free incident detection algorithm with traffic speed data on freeways was proposed, and the results were compared with other commonly used algorithms. Third, a spatial and temporal quality analysis of Waze accident reports to better understand their quality and accuracy was performed. Last, the dissertation proposed a network-based clustering algorithm to identify secondary crashes with Waze user reports, and a case study was performed to demonstrate the applicability of our method and the potential of crowdsourced Waze user reports

Integrated Approach for Diversion Route Performance Management during Incidents

Non-recurrent congestion is one of the critical sources of congestion on the highway. In particular, traffic incidents create congestion in unexpected times and places that travelers do not prepare for. During incidents on freeways, route diversion has been proven to be a useful tactic to mitigate non-recurrent congestion. However, the capacity constraints created by the signals on the alternative routes put limits on the diversion process since the typical time-of-day signal control cannot handle the sudden increase in the traffic on the arterials due to diversion. Thus, there is a need for proactive strategies for the management of the diversion routes performance and for coordinated freeway and arterial (CFA) operation during incidents on the freeway. Proactive strategies provide better opportunities for both the agency and the traveler to make and implement decisions to improve performance. This dissertation develops a methodology for the performance management of diversion routes through integrating freeway and arterials operation during incidents on the freeway. The methodology includes the identification of potential diversion routes for freeway incidents and the generation and implementation of special signal plans under different incident and traffic conditions. The study utilizes machine learning, data analytics, multi-resolution modeling, and multi-objective optimization for this purpose. A data analytic approach based on the long short term memory (LSTM) deep neural network method is used to predict the utilized alternative routes dynamically using incident attributes and traffic status on the freeway and travel time on both the freeway and alternative routes during the incident. Then, a combination of clustering analysis, multi- resolution modeling (MRM), and multi-objective optimization techniques are used to develop and activate special signal plans on the identified alternative routes. The developed methods use data from different sources, including connected vehicle (CV) data and high- resolution controller (HRC) data for congestion patterns identification at the critical intersections on the alternative routes and signal plans generation. The results indicate that implementing signal timing plans to better accommodate the diverted traffic can improve the performance of the diverted traffic without significantly deteriorating other movements\u27 performance at the intersection. The findings show the importance of using data from emerging sources in developing plans to improve the performance of the diversion routes and ensure CFA operation with higher effectiveness

Developing Sampling Strategies and Predicting Freeway Travel Time Using Bluetooth Data

Accurate, reliable, and timely travel time is critical to monitor transportation system performance and assist motorists with trip-making decisions. Travel time is estimated using the data from various sources like cellular technology, automatic vehicle identification (AVI) systems. Irrespective of sources, data have characteristics in terms of accuracy and reliability shaped by the sampling rate along with other factors. As a probe based AVI technology, Bluetooth data is not immune to the sampling issue that directly affects the accuracy and reliability of the information it provides. The sampling rate can be affected by the stochastic nature of traffic state varying by time of day. A single outlier may sharply affect the travel time. This study brings attention to several crucial issues - intervals with no sample, minimum sample size and stochastic property of travel time, that play pivotal role on the accuracy and reliability of information along with its time coverage. It also demonstrates noble approaches and thus, represents a guideline for researchers and practitioner to select an appropriate interval for sample accumulation flexibly by set up the threshold guided by the nature of individual researches’ problems and preferences. After selection of an appropriate interval for sample accumulation, the next step is to estimate travel time. Travel time can be estimated either based on arrival time or based on departure time of corresponding vehicle. Considering the estimation procedure, these two are defined as arrival time based travel time (ATT) and departure time based travel time (DTT) respectively. A simple data processing algorithm, which processed more than a hundred million records reliably and efficiently, was introduced to ensure accurate estimation of travel time. Since outlier filtering plays a pivotal role in estimation accuracy, a simplified technique has proposed to filter outliers after examining several well-established outlier-filtering algorithms. In general, time of arrival is utilized to estimate overall travel time; however, travel time based on departure time (DTT) is more accurate and thus, DTT should be treated as true travel time. Accurate prediction is an integral component of calculating DTT, as real-time DTT is not available. The performances of Kalman filter (KF) were compared to corresponding modeling techniques; both link and corridor based, and concluded that the KF method offers superior prediction accuracy in link-based model. This research also examined the effect of different noise assumptions and found that the steady noise computed from full-dataset leads to the most accurate prediction. Travel time prediction had a 4.53% mean absolute percentage of error due to the effective application of KF

Towards Universality in Automatic Freeway Incident Detection: A Calibration-Free Algorithm

Freeway automatic incident detection (AID) algorithms have been extensively investigated over the last forty years. A myriad of algorithms, covering a broad range of types in terms of complexity, data requirements, and efficiency have been published in the literature. However, a 2007 nationwide survey concluded that the implementation of AID algorithms in traffic management centers is still very limited. There are a few reasons for this discrepancy between the state-of-the-art and the state-of the-practice. First, current AID algorithms yield unacceptably high rates of false alarm when implemented in real-world. Second, the complexities involved in algorithm calibration require levels of efforts and diligence that may overburden Traffic Management Center (TMC) personnel. The main objective of this research was to develop a self-learning, transferable algorithm that requires no calibration. The dynamic thresholds of the proposed algorithm are based on historical data of traffic, thus accounting for variations of traffic throughout the day. Therefore, the novel approach is able to recognize recurrent congestion, thus greatly reducing the incidence of false alarms. In addition, the proposed method requires no human-intervention, which certainly encourages its implementation. The presented model was evaluated in a newly developed incident database, which contained forty incidents. The model performed better than the California, Minnesota, and Standard Normal Deviation algorithms

Incident Detection Algorithm Evaluation

This research examines a range of incident detection technologies to determine a recommended combination of approaches for use in the Utah Department of Transportation (UDOT) Advanced Traffic Management System (ATMS). The technologies that were examined are computer-based Automatic Incident Detection (AID), Video Image Processing (VIP), and detection by cellular telephone call-ins

Detection of traffic events from Finnish social media data

Social media has gained significant popularity and importance during the past few years and has become an essential part of many people s everyday lives. As social media users write about a broad range of topics, popular social networking sites can serve as a perfect base for various data mining and information extraction applications. One possibility among these could be the real-time detection of unexpected traffic events or anomalies, which could be used to help traffic managers to discover and mitigate problematic spots in a timely manner or to assist passengers with making informed decisions about their travel route. The purpose of this study is to develop a Finnish traffic information system that relies on social media data. The potential of using social network streams in traffic information extraction has been demonstrated in several big cities, but no study has so far investigated the possible use in smaller communities such as towns in Finland. The complexity of Finnish language also poses further challenges. The aim of the research is to investigate what methods would be the most suitable to analyse and extract information from Finnish social media messages and to incorporate these into the implementation of a practical application. In order to determine the most effective methods for the purposes of this study, an extensive literature research was performed in the fields of social media mining and textual and linguistic analysis with a special focus on frameworks and methods designed for Finnish language. In addition, a website and a mobile application were developed for data collection, analysis and demonstration. The implemented traffic event detection system is able to detect and classify incidents from the public Twitter stream. Tests of the analysis methods have determined high accuracy both in terms of textual and cluster analysis. Although certain limitations and possible improvements should be considered in the future, the ready traffic information system has already demonstrated satisfactory performance and lay the foundation for further studies and research

Intelligent Real-Time Decision Support Systems for Road Traffic Management. Multi-agent based Fuzzy Neural Networks with a GA learning approach in managing control actions of road traffic centres.

The selection of the most appropriate traffic control actions to solve non-recurrent traffic congestion is a complex task which requires significant expert knowledge and experience. In this thesis we develop and investigate the application of an intelligent traffic control decision support system for road traffic management to assist the human operator to identify the most suitable control actions in order to deal with non-recurrent and non-predictable traffic congestion in a real-time situation. Our intelligent system employs a Fuzzy Neural Networks (FNN) Tool that combines the capabilities of fuzzy reasoning in measuring imprecise and dynamic factors and the capabilities of neural networks in terms of learning processes. In this work we present an effective learning approach with regard to the FNN-Tool, which consists of three stages: initializing the membership functions of both input and output variables by determining their centres and widths using self-organizing algorithms; employing an evolutionary Genetic Algorithm (GA) based learning method to identify the fuzzy rules; tune the derived structure and parameters using the back-propagation learning algorithm. We evaluate experimentally the performance and the prediction capability of this three-stage learning approach using well-known benchmark examples. Experimental results demonstrate the ability of the learning approach to identify all relevant fuzzy rules from the training data. A comparative analysis shows that the proposed learning approach has a higher degree of predictive capability than existing models. We also address the scalability issue of our intelligent traffic control decision support system by using a multi-agent based approach. The large network is divided into sub-networks, each of which has its own associated agent. Finally, our intelligent traffic control decision support system is applied to a number of road traffic case studies using the traffic network in Riyadh, in Saudi Arabia. The results obtained are promising and show that our intelligent traffic control decision support system can provide an effective support for real-time traffic control

Design and validation of novel methods for long-term road traffic forecasting

132 p.Road traffic management is a critical aspect for the design and planning of complex urban transport networks for which vehicle flow forecasting is an essential component. As a testimony of its paramount relevance in transport planning and logistics, thousands of scientific research works have covered the traffic forecasting topic during the last 50 years. In the beginning most approaches relied on autoregressive models and other analysis methods suited for time series data. During the last two decades, the development of new technology, platforms and techniques for massive data processing under the Big Data umbrella, the availability of data from multiple sources fostered by the Open Data philosophy and an ever-growing need of decision makers for accurate traffic predictions have shifted the spotlight to data-driven procedures. Even in this convenient context, with abundance of open data to experiment and advanced techniques to exploit them, most predictive models reported in literature aim for shortterm forecasts, and their performance degrades when the prediction horizon is increased. Long-termforecasting strategies are more scarce, and commonly based on the detection and assignment to patterns. These approaches can perform reasonably well unless an unexpected event provokes non predictable changes, or if the allocation to a pattern is inaccurate.The main core of the work in this Thesis has revolved around datadriven traffic forecasting, ultimately pursuing long-term forecasts. This has broadly entailed a deep analysis and understanding of the state of the art, and dealing with incompleteness of data, among other lesser issues. Besides, the second part of this dissertation presents an application outlook of the developed techniques, providing methods and unexpected insights of the local impact of traffic in pollution. The obtained results reveal that the impact of vehicular emissions on the pollution levels is overshadowe

Detection of traffic events from Finnish social media data

Social media has gained significant popularity and importance during the past few years and has become an essential part of many people s everyday lives. As social media users write about a broad range of topics, popular social networking sites can serve as a perfect base for various data mining and information extraction applications. One possibility among these could be the real-time detection of unexpected traffic events or anomalies, which could be used to help traffic managers to discover and mitigate problematic spots in a timely manner or to assist passengers with making informed decisions about their travel route. The purpose of this study is to develop a Finnish traffic information system that relies on social media data. The potential of using social network streams in traffic information extraction has been demonstrated in several big cities, but no study has so far investigated the possible use in smaller communities such as towns in Finland. The complexity of Finnish language also poses further challenges. The aim of the research is to investigate what methods would be the most suitable to analyse and extract information from Finnish social media messages and to incorporate these into the implementation of a practical application. In order to determine the most effective methods for the purposes of this study, an extensive literature research was performed in the fields of social media mining and textual and linguistic analysis with a special focus on frameworks and methods designed for Finnish language. In addition, a website and a mobile application were developed for data collection, analysis and demonstration. The implemented traffic event detection system is able to detect and classify incidents from the public Twitter stream. Tests of the analysis methods have determined high accuracy both in terms of textual and cluster analysis. Although certain limitations and possible improvements should be considered in the future, the ready traffic information system has already demonstrated satisfactory performance and lay the foundation for further studies and research