Data-Driven Approaches for Assessing the Impact of Speed Management Strategies for Arterial Mobility and Safety

Arterials are the connector of the national transportation system to regional mobility. Arterials play a significant role in providing accessibility to residential and commercial neighborhoods. Therefore, they are essential to the regional economy and residents' quality of life. Through the Moving Ahead for Progress in the 21st Century Act (MAP-21), US Congress requires that all the state Departments of Transportation (DOTs) and Multimodal Planning Organizations (MPOs) monitor, improve, and maintain the mobility and safety performance of their jurisdiction’s road network. A simple and straightforward solution to improve arterial mobility and safety would be constructing new roads. However, due to the limited infrastructure and high construction cost, this solution is not always feasible. One viable solution to enhance the mobility and safety of arterials is using ITS technologies. Speed management strategies are one of the emerging ITS technologies that are currently been utilized by different state departments of transportation to improve the safety and mobility of their road network. This dissertation focuses on proposing comprehensive data-driven speed management strategies and evaluating their impact on the mobility and safety of signalized arterials. This dissertation consists of the following components. One important issue before conducting any traffic studies is validating traffic data quality. Low quality and incomplete traffic data will negatively impact traffic projects’ outcomes. This component of the dissertation aims to develop a data-driven hybrid model to impute the missing and incomplete values. The proposed model imputes missing values by considering the interaction, similarity, and differences of the data as well as incorporating available historical information. The application of the proposed model was used to impute missing truck travel time data in the National Performance Measures Research Dataset (NPMRDS). The analysis result showed that the proposed model is able to impute severe continuous missing data with high accuracy. The comparison results also showed the proposed model will outperform other conventional models while dealing with severe missing conditions. The clean and high-quality data achieved from the first component will be used for identifying the segments with speeding problems and then identifying appropriate speed management strategies. A considerable amount of research has demonstrated a direct relationship between speed and both crash frequency and crash severity. Therefore, speed management strategies to impose the speed limit and tackle speeding are important for transportation agencies to improve mobility and safety at the corridor level. In this component of the dissertation the effects of implementing several speed management strategies, namely speed feedback signs, periodic law enforcement, and speed feedback sign supported with periodic law enforcement on driver speed behavior and compliance was examined. To analyze the effectiveness of each strategy, nine locations in Pima County, Arizona, were selected in a cross-sectional framework. The results of this component showed that supporting SFS with periodic law enforcement could be a key speed management strategy that takes advantage of the strengths of both SFS and law enforcement. Further, the results showed the existence of periodic law enforcement could potentially modify drivers’ behaviors and increase the spatial effectiveness of speed feedback signs. When it comes to mobility and safety evaluation of speed management strategies, previous studies only focused more on the corridor level instead of breaking the evaluation into the link (the segment between two intersections) and the intersection levels. Furthermore, the majority of the studies used historical crash data to investigate the safety impact of speed management strategies. Collecting such data takes time and effort. Therefore, this component of the dissertation focuses more on evaluating the impact of speed feedback sign at both link and intersections levels. The application of this component was implemented on a west/east arterial in Pima County, AZ. The results of intersection level data analysis showed no statistically significant differences in either mean or variance of the signalized performance measures before and after disabling the speed feedback sign. Moreover, it was found that the impact of speed feedback sign on driver’s behavior is a function of their approaching speed. Finally, the benefit in dollar value per year associated with a reduction in severe crashes on the study arterial with active SFS showed promising safety enhancement. Traffic signal retiming is another effective speed management strategy that significantly impacts the overall mobility of signalized arterials. Implementing correct signal timing plans and periodically retiming them will result in direct and indirect benefits such as reduction in the delay and travel time as direct benefits, and reduction in fuel consumption, air pollution, pavement wear, and tear as indirect benefits. In this component of the dissertation, a systematic step-wise approach is proposed that can assists transportation agencies to frequently fine-tune their signal timing parameters, rather than retiming the whole corridor every three to five years. In addition, the proposed approach will allow transportation agencies to predict the intersection mobility performance prior to the field implementation. The application of the proposed approach was implemented on multiple intersections on a major corridor in Pima County, Arizona. The prediction results showed that by only fine-tuning the green split, we are able to achieve on average 10% improvement on the intersection simple delay. The outcome of this dissertation could help DOTs and MPOs to use ITS technologies, to monitor, improve, and maintain the mobility and safety performance of their jurisdiction’s road network. More specifically, the data-driven safety and mobility approaches conducted in this study could: 1-provide a framework for missing data imputation before conducting traffic studies, 2-help transportation agencies to use the high-quality data to identify the high-risk location and identify the appropriate speed management strategy, 3- analyze the impact of speed management strategies at the corridor and intersection level, and 4- help transportation agencies with a more efficient way for signal retiming, enhance arterial mobility and consequently save money and resources

Webinar: Data-Driven Mobility Strategies for Multimodal Transportation

Multimodal transportation systems (e.g., walking, cycling, automobile, public transit, etc.) are effective in increasing people’s travel flexibility, reducing congestion, and improving safety. Therefore, it is critical to understand what factors would affect people’s mode choices. With advanced technology, such as connected and automated vehicles, cities are now facing a transition from traditional urban planning to developing smart cities. To support multimodal transportation management, this study serves as a bridge to connect speed management strategies of conventional corridors to connected vehicle corridors. The study consists of three main components. In the first component, the impact of speed management strategies along traditional corridors was evaluated. In the second component, the impacts of the specific speed management strategies, signal retiming and coordination, on transit signal priority (TSP) was studied. Finally, in the third component, the feasibility of using controller event-based traffic data for estimating multimodal signal performance measures was investigated. The research outcomes of this study will help decision-makers understand the data and infrastructure needs in supporting future multimodal planning, operation, and safety tasks.https://pdxscholar.library.pdx.edu/trec_webinar/1063/thumbnail.jp

Data-Driven Mobility Strategies for Multimodal Transportation

Multimodal transportation systems (e.g., walking, cycling, automobile, public transit, etc.) are effective in increasing people’s travel flexibility, reducing congestion, and improving safety. Therefore, it is critical to understand what factors would affect people’s mode choices. With advanced technology, such as connected and automated vehicles, cities are now facing a transition from traditional urban planning to developing smart cities. To support multimodal transportation management, this study will serve as a bridge to connect speed management strategies of conventional corridors to connected vehicle corridors. This study consists of three main components. In the first component, the impact of speed management strategies along traditional corridors was evaluated. To do so, a study corridor in Pima County, AZ, was selected, and using the data collected from smart sensors, the mobility and safety impact of a specific speed management strategy was explored. The results of this component showed a positive impact of SFS on both mobility and safety along traditional corridors. In the second component, the impacts of the specific speed management strategies, signal retiming and coordination, on transit signal priority (TSP) was studied. A connected corridor in Salt Lake City, UT, was selected as the study corridor. The results of this component showed TSP has great potential to reduce bus delays at intersections, improve transit operational reliability, and consequently increase transit ridership with improved service. Finally, in the third component, the feasibility of using controller event-based traffic data for estimating multimodal signal performance measures was investigated. Four intersections on Ina Rd., Pima County were selected as the study locations. The results of this component showed the proposed delay estimation method was able to capture and track the actual delay fluctuation during the day with an average of 10% of mean absolute error. The research outcomes of this study will help decision-makers understand the data and infrastructure needs in supporting future multimodal planning, operation, and safety tasks

Estimating Pedestrian Delay at Signalized Intersections Using High-Resolution Event-Based Data: a Finite Mixture Modeling Method

It has been widely shown that pedestrians’ level of frustration grows with the increase of pedestrian delay, and may cause pedestrians to violate the signals. However, for agencies seeking to use multimodal signal performances for signal operations, the pedestrian delay is not always readily available. To tackle this issue, this study proposed a finite mixture modeling method to estimate pedestrian delay using high-resolution event-based data collected from the smart sensors. The proposed method was used to estimate pedestrian delay at four signalized intersections on a major arterial corridor in Pima County, Arizona. The results showed the proposed method was able to capture and track the actual pedestrian delay fluctuations during the day at all the study intersections with average errors of 10 s and 13 s for mean-absolute-error and root-mean-square-error, respectively. In addition, the proposed model was compared with three conventional methods (HCM 2010, Virkler, Dunn) and the comparison results showed that the proposed method outperforms all the other methods in terms of both mean-absolute-error and root-mean-square-error. Furthermore, it was found that the proposed method is transferable and can be used as a network-wide delay estimation model for intersections with similar traffic patterns. The application of the proposed method could provide agencies with a more reliable, robust, and yet accurate approach for estimating pedestrian delay at signalized intersections where the pedestrian data are not readily available. In addition, it will allow system operators to quantitatively assess existing delays and enact changes to incorporate the better serve pedestrian needs

Handling Imbalanced Data for Real-Time Crash Prediction: Application of Boosting and Sampling Techniques

With a growing number of intelligent transportation system sensors and the networkwide deployment of those across the nation’s roadway facilities, current research and practices should concentrate on more proactive safety strategies. In recent years, real-time traffic data collected from ITS sensors have been utilized to develop crash prediction models. Real-time crash prediction models can be used to identify hazardous traffic conditions that might cause a crash. This study aims to examine how employing data mining techniques that account for imbalanced data could improve the predictive capability of real-time crash prediction models. The term imbalanced data refers to a condition where the number of observations in each class is not equally distributed among the data set (noncrash cases outnumber crash cases). To decrease the within-class variation of imbalanced data, the data were split into two traffic-state data sets: free-flow speed (FFS) and congestion. Three models, including logistic regression as the baseline, random forest (RF) with random undersampling, and Adaptive Boosting (AdaBoost), were estimated with each data set. The results were compared with the models that were estimated using the complete set of data. Model comparisons indicated that all three models achieved significantly better predictive results with the congested and FFS data sets as opposed to the data set containing all crashes and that, while in some cases the results of the undersampled RF model were slightly better than those of AdaBoost, both models outperformed the logistic regression model. The results of this study demonstrated that using models to deal with imbalanced data and lowering the variation of imbalanced data could substantially improve crash prediction accuracy. The findings could help traffic agencies to practically implement and deploy crash prediction models for real-time applications and develop crash prevention strategies accordingly

A Theoretical and Experimental Analysis of the Effect of Nanoclay on Gas Perm-Selectivity of Biodegradable PLA/EVA Blends in the Presence and Absence of Compatibilizer

Poly (lactic acid) (PLA)-based compounds are widely used in thin-film and food packaging industries. Herein, PLA/ethylene vinyl acetate copolymer (EVA)/nanoclay nanocomposites are prepared in various compositions by melt blending. The gas permeability against N2, CO2, and O2 gases is determined as a function of composition and morphology of the nanocomposites. Inclusion of high aspect ratio of platelet-like nanoclay to the blend reduces the gas diffusion. The best barrier properties against all gases is observed on introducing 5 wt% poly(ethylene/n-butyl acrylate glycidyl methacrylate) copolymer as compatibilizer to the PLA/EVA/nanoclay (75/25/5) system. The scanning and transmission electron microscopic analyses and wide-angle X-ray scattering studies reveal that inclusion of compatibilizer to the filled-blends improves the blend morphology, dispersion state, and intercalation level of clay platelets which are preferably localized at the interface of the blend. Analysis of selectivity parameter (a) shows the lowest O2 permeability and the highest aCO2/N2 and aO2/N2 values for the compatibilized filled-blend (75/25/5/5). In situ aspect ratio of clay and the degree of intercalation are theoretically evaluated based on the permeability data using various empirical models. It is found that the compatibilized filled-blend has the highest aspect ratio and intercalation level that are responsible for the optimum perm-selectivity performance. © 2020 The Authors. Published by Wiley-VCH Gmb