FCG-ASpredictor: An Approach for the Prediction of Average Speed of Road Segments with Floating Car GPS Data

The average speed (AS) of a road segment is an important factor for predicting traffic congestion, because the accuracy of AS can directly affect the implementation of traffic management. The traffic environment, spatiotemporal information, and the dynamic interaction between these two factors impact the predictive accuracy of AS in the existing literature, and floating car data comprehensively reflect the operation of urban road vehicles. In this paper, we proposed a novel road segment AS predictive model, which is based on floating car data. First, the impact of historical AS, weather, and date attributes on AS prediction has been analyzed. Then, through spatiotemporal correlations calculation based on the data from Global Positioning System (GPS), the predictive method utilizes the recursive least squares method to fuse the historical AS with other factors (such as weather, date attributes, etc.) and adopts an extended Kalman filter algorithm to accurately predict the AS of the target segment. Finally, we applied our approach on the traffic congestion prediction on four road segments in Chengdu, China. The results showed that the proposed predictive model is highly feasible and accurate. Document type: Articl

Surrogate safety measures and traffic conflict observations.

The chapter primarily focuses on observing traffic conflicts (also known as near-accidents) as a site-based road safety analysis technique. Traffic conflicts are a type of surrogate safety measure. The term surrogate indicates that non-accident-based indicators are used to assess VRU safety instead ofthe more traditional approach focusing on accidents (see chapter 2). The theory underpinning surrogate safety measures is briefly described, followed by a discussion on the characteristics of the traffic conflict technique. Next, guidelines for conducting traffic conflict observations using trained human observers or video cameras are presented. Chapter 4 concludes with examples of the use of the traffic conflict technique in road safety studies focusing on VRUs

Traffic Speed Prediction and Mobility Behavior Analysis Using On-Demand Ride-Hailing Service Data

Providing accurate traffic speed prediction is essential for the success of Intelligent Transportation Systems (ITS) deployments. Accurate traffic speed prediction allows traffic managers take proper countermeasures when emergent changes happen in the transportation network. In this thesis, we present a computationally less expensive machine learning approach XGBoost to predict the future travel speed of a selected sub-network in Beijing\u27s transportation network. We perform different experiments for predicting speed in the network from future 1 min to 20 min. We compare the XGBoost approach against other well-known machine learning and statistical models such as linear regression and decision tree, gradient boosting tree, and random forest regression models. Three metrics MAE, MAPE, and RMSE are used to evaluate the performance of the selected models. Our results show that XGBoost outperforms other models across different experiment conditions. Based on the prediction accuracy of different links, we find that the number of vehicles operating in a network also affect prediction performance. In addition, understanding individual mobility behavior is critical for modeling urban dynamics. It provides deeper insights on the generative mechanisms of human movements. Recently, different types of emerging data sources such as mobile phone call detail records, social media posts, GPS observations, and smart card transactions have been used to analyze individual mobility behavior. In this thesis, we report the spatio-temporal mobility behaviors using large-scale data collected from a ride-hailing service platform. Based on passenger-level travel data, we develop an algorithm to identify users\u27 visited places and the functions of those places. To characterize temporal movement patterns, we reveal the differences in trip generation characteristics between commuting and non-commuting trips and the distribution of gap time between consecutive trips. To understand spatial mobility patterns, we observe the distribution of the number of visited place and their rank, the spatial distribution of residences and workplaces, and the distribution of travel distance and travel time. Our analysis highlights the differences in mobility patterns of the users of ride-hailing services, compared to the findings of existing mobility studies based on other data sources. Our study shows a tremendous potential of developing high-resolution individual-level mobility model that can predict the demand of emerging mobility services with high accuracy

An architectural framework for self-configuration and self-improvement at runtime

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Performance-Based Operations Assessment of Adaptive Control Implementation in Des Moines, Iowa RB09-016, August 2018

Maintaining optimal mobility on high-volume arterial traffic corridors is important to transportation agencies and the public. Corridor performance often can be enhanced by updating traffic signal timing, but most agencies find it necessary to prioritize their retiming efforts based on resource constraints. To facilitate prioritization, a set of arterial corridor performance measures was developed using INRIX probe vehicle data. These commercially available data are derived from in-vehicle global positioning system (GPS) observations transmitted wirelessly, eliminating the need for supplemental traffic observation infrastructure to be installed in the field. The main objective of this study was to present a methodology to compare arterial corridors in terms of mobility-based performance measures. This process can help agencies select the corridors that are in need of signal retiming and can help identify corridors suited for adaptive signal control implementation. The two-step methodology began by identifying the number of days in a year with abnormal traffic patterns and comparing the volume-normalized performance of the remaining segments to identify corridors that are problematic on normal days. The proposed methodology was applied to 12 corridors in Des Moines, Iowa, and 1 in Omaha, Nebraska. Three corridors were found to have a high number of anomalous days. Among the remaining corridors, three were identified as under-performing on normal days. In addition, the impact of implementing an adaptive signal control system on one corridor (University Avenue) was evaluated, where small improvements in travel rate and daily variation were observed, but the overall variability increased

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