Li Zhao’s research while affiliated with University of Nebraska–Lincoln and other places

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Publications (21)


Data Accuracy Matters: Improving Highway-Rail Grade Crossings Crash Predictions through Inventory Verification
  • Article

August 2024

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11 Reads

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1 Citation

Transportation Research Record Journal of the Transportation Research Board

Li Zhao

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Aemal J Khattak

Highway-rail grade crossing (HRGC) crash prediction models’ effectiveness hinges on the input data accuracy and precision. This paper investigates the impact of inaccurate HRGC inventory data on the modeling of HRGC crashes. Specifically, the research explores data gaps by obtaining samples of Federal Railroad Administration rail crossing inventory data. These inventory data were checked for accuracy by visiting the rail crossings and comparing the inventory elements to their field conditions. Any inaccurate records were corrected; the process created an accurate inventory of the rail crossings under consideration. The corrected inventory data was subsequently used for crash predictions using the U.S. Department of Transportation accident prediction formula (U.S. DOT APF), released in 2020. To fit for the U.S. DOT APF, the corrected inventory data from Nebraska was used for the case 1 study, which applied a multiple imputation algorithm to augment the empirical data to verify improvements in the model’s goodness of fit. The results showed that the adjusted Akaike information criterion (AIC) improved from 1,074 to 1,068 when only 7% of the total inventory dataset was corrected, and to 813 assuming all verified corrected data obtained through data imputation. In case 2, the filtered inventory data from four Midwest states (i.e., Kansas, Iowa, Missouri, and Nebraska) were utilized to address data stratification issues in the U.S. DOT APF. Results showed that the adjusted AIC improved from 1,442 to 1,431 when the latest annual average daily traffic data and properly stratified variables (i.e., road surface, traffic control) were included in the U.S. DOT APF. The findings emphasize the need for regular HRGC inventory data verification and improved data-updating processes for more accurate HRGC crash predictions.


FIGURE 2: Nebraska data for Benefit Calculation
FIGURE 3: Mail Survey Distribution
Benefit Calculation Method for Each Census Tract
Benefit Metric Weights
Global Moran's I Statistics for Different Benefit Categories.
Framework for Quantifying Benefits of Electric Vehicle Charging Infrastructure to Disadvantaged Communities
  • Preprint
  • File available

August 2024

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87 Reads

The Justice40 Initiative mandates that 40% of the benefits from federal investments in clean transportation flow to disadvantaged communities (DACs) to ensure equitable access. While each state is responsible for implementing this directive in electric vehicle charging infrastructure (EVI) investments through their proposed National Electric Vehicle Infrastructure (NEVI) plans, none have quantified and measured the benefits that flow to DACs. This research addresses this gap by identifying four key metrics (accessibility-based energy efficiency, economic growth, climate change, and environmental and health improvements) and quantifying their benefits among census tracts as result of EVI deployment. Importantly, a comprehensive framework is developed for each of the four EVI benefits, covering their generation in census tracts, distribution among census tracts, assignment to census tracts, and a composite accumulation within census tracts. Global Moran's I analysis is used as a performance measure to reveal spatial clustering in the distribution of the overall benefits. To demonstrate the application of the developed framework, it is applied to quantify the benefits of deploying EVI in Nebraska, where census data, field data, and statewide survey data were collected for this purpose. The research outcomes offer practical guidance for implementing transportation equity projects under Justice40 requirements. They provide insights into how federal and local investments can address the diverse needs of communities and ensure that benefits are effectively directed to DACs.

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Safety and Mobility Improvement at Highway-rail Grade Crossings Using Real-Time Optimized Preemption of Traffic Signal Strategies

This research project embarked on a crucial endeavor to enhance safety and efficiency at highway-rail grade crossings (HRGCs) through the innovative development and application of real-time optimized traffic signal preemption strategies. Recognizing the significant risks associated with HRGCs, especially in urban areas where such crossings are in close proximity to signalized intersections, this study aimed to address the complexities of traffic flow and preemptive signal operations to improve both safety and mobility. The project progressed through the completion of four major tasks: 1) Review and Identification of Limitations: Conducting a holistic review of existing preemption operations, national guidelines, and current engineering practices, the study began with studying in current HRGC preemption strategies. 2) Effectiveness Verification: Through the development of microsimulation models and sensitivity analysis, the project rigorously tested the efficacy of various preemption plans across different HRGC scenarios. 3) Standard Optimization Process: Aiming to maximize safety and operational efficiency, a standard optimization process for designing preemption strategies was developed. 4) Guideline Development: A significant outcome of the project was the development of a guideline that provides a standardized process for evaluating the effectiveness of signal control at HRGCs and adjacent arterials. This research represents a significant step forward in traffic safety and efficiency management at HRGCs, providing a model for similar traffic situations in other regions and laying the groundwork for future technological advancements in the field. The developed guideline serves to offer technical support in terms of application conditions, plan formation, and system operations, aiming to facilitate implementation while enhancing coordination between railway and highway agencies.


Sentiments of Rural U.S. Communities on Electric Vehicles and Infrastructure: Insights from Twitter Data

June 2024

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31 Reads

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1 Citation

Sustainability

The widespread adoption of electric vehicles (EVs) and the development of charging infrastructure is key to achieving sustainable transportation and reducing greenhouse emissions. This research paper presents a novel exploration of the public sentiments expressed by rural U.S. communities toward EVs and EV infrastructure using Twitter data. To understand the factors influencing public sentiment, three distinct models were developed and applied: Generalized Linear Models, Hierarchical Linear Models, and Geographically Weighted Regression. These models explored the relationships between sentiment and several impact factors, including the topics of the tweets, and the age and sex of tweet senders as well as the number of charging stations and historical accident data in the geographical vicinity of each tweet’s origin. Results indicate that a more positive sentiment on EVs resulted (1) when the tweet discussed EV infrastructure investment and equity, (2) when the tweeter was male, and (3) when more charging stations were present and fewer EV accidents occurred in the county, especially in rural areas. Counties with higher rural percentages generally exhibited more positive sentiments toward EV usage. The paper contributes to the existing literature by shedding light on the sentiments of rural residents toward EVs and the infrastructure.


Improving Highway-Rail Grade Crossing Crash Prediction Models by Addressing Crossing Inventory Data Accuracy

January 2024

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7 Reads

Highway-rail grade crossing (HRGC) crash prediction models’ effectiveness hinges on the input data accuracy and precision. This paper investigates the impact of inaccurate HRGC inventory data on the modeling of HRGC crashes. In specific, the research explores existing data gaps by obtaining samples of Federal Railroad Administration (FRA) rail crossing inventory data, which contains some inaccurate records. Those inaccurate records were corrected by visiting the HRGCs, and the crossing corrected inventory data used for crash predictions using the U.S. Department of Transportation recommended accident prediction formula (U.S. 2020 DOT APF) for HRGCs. To fit for the U.S. DOT APF, the corrected inventory data from Nebraska was used for case 1 study, which applied a multiple imputation algorithm to augment the empirical data to verify improvements in the model’s goodness-of-fit. The results show that the adjusted AIC improves from 1074 to 1068 when only 7% of the total inventory dataset is corrected, and to 813 assuming all verified corrected data obtained through data imputation. In case 2, the filtered inventory data from four Midwest states (i.e., Kansas, Iowa, Missouri, and Nebraska) were utilized to address data stratification issues in the U.S. DOT APF. The results show that the adjusted AIC improves from 1442 to 1431 when adopting latest AADT data and properly stratified variables (i.e., road surface, traffic control) are included in the U.S. DOT APF. The findings emphasize the need for regular HRGC inventory data verification and improved data-updating processes for robust traffic crash prediction modeling for HRGCs.



Gate violation prediction at highway-rail grade crossings using tree-based ensemble techniques

January 2023

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16 Reads

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2 Citations

Journal of Transportation Safety & Security

Highway-rail grade crossing (HRGC) safety is one of the priority areas in the United States transportation system that requires for greater research efforts not just limited to crash analysis, but also to gain a deeper understanding of surrogate safety measures such as driver behavior-based traffic violations at HRGCs. This paper uses vehicle profile data to identify the key variables and develop prediction models for gate violations and examine the relationship between model accuracy and the key input variables. A data set of 256 vehicle-train events was collected at two HRGC testbeds in Lincoln, Nebraska. Among them, 76 events are gate violations, and 180 events are non-violations. Two tree-based ensemble techniques, the bootstrap forest and the boosted tree, were applied to the data set. It was found that once a vehicle is within 190 feet from the HRGC stop line, the model was approximately 80 percent accurate in predicting a gate violation. It was also found that as vehicles came closer to the HRGC, the prediction error decreased. With the advent of vehicle profile data collection, tree-based ensemble techniques are ideal for safety studies as they can utilize the highly non-linear vehicle profiles and relate these to safety surrogate metrics.


Impact of Platooning Connected and Automated Heavy Vehicles on Interstate Freeway Work Zone Operations

December 2022

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29 Reads

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5 Citations

Journal of Transportation Engineering Part A Systems

Work zones pose mobility issues to the traveling public and safety challenges to travelers and road maintenance workers. These safety and mobility issues may be exacerbated by the presence of heavy vehicles. Connected and automated vehicle (CAV) technologies have been identified as a potential solution for these issues. This paper investigates the operational impacts of connected and automated heavy vehicles (CAHV) on freeway work zone operations on interstate highways. A microsimulation model, calibrated to empirical work zone field data, was used to study the operational impacts of CAHV platoons under various work zone and traffic conditions. It was found that, as the CAHV market penetration rate increases, the average work zone delay and queue length decreases. In addition, as the demand and heavy vehicle percentage increases, so do the benefits of using CAHV technology. For example, it was found that, when all heavy vehicles are classified as CAHV, the average flow rate is approximately 67% higher, and the maximum queue size and average delay decrease by approximately 97%. The methodology used in this paper will help transportation agencies as they design work zones to accommodate heavy vehicles equipped with CAV technologies.


Exploration of the characteristics and trends of electric vehicle crashes: a case study in Norway

December 2022

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693 Reads

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19 Citations

European Transport Research Review

With the rapid growth of electric vehicles (EVs) in the past decade, many new traffic safety challenges are also emerging. With the crash data of Norway from 2011 to 2018, this study gives an overview of the status quo of EV crashes. In the survey period, the proportion of EV crashes in total traffic crashes had risen from zero to 3.11% in Norway. However, in terms of severity, EV crashes do not show statistically significant differences from the Internal Combustion Engine Vehicle (ICEV) crashes. Compared to ICEV crashes, the occurrence of EV crashes features on weekday peak hours, urban areas, roadway junctions, low-speed roadways, and good visibility scenarios, which can be attributed to the fact that EVs are mainly used for urban local commuting travels in Norway. Besides, EVs are confirmed to be much more likely to collide with cyclists and pedestrians, probably due to their low-noise engines. Then, the separate logistic regression models are built to identify important factors influencing the severity of ICEV and EV crashes, respectively. Many factors show very different effects on ICEV and EV crashes, which implies the necessity of re-evaluating many current traffic safety strategies in the face of the EV era. Although the Norway data is analyzed here, the findings are expected to provide new insights to other countries also in the process of the complete automotive electrification.


Developing a Safety Management System including Hazardous Materials for Highway-Rail Grade Crossings in Region VII

Highway-rail grade crossings (HRGCs) rank among the leading locations for fatal crashes on the railroad network in the United States, and safety at HRGCs is a top priority for the railroads and the Federal Railroad Administration (FRA). This project studied HRGC safety needs by investigating crash data and HRGC inventory characteristics, and then developed a systematic framework for HRGC safety management in three steps. First, the project started with preparing a comprehensive database that included 1) HRGC crashes with geographic coordinates, 2) HRGC inventory data, 3) highway and train traffic operations data, and 4) HRGC crash-related hazardous materials release data. These data were obtained from the FRA and spanned across the four states in Region VII, i.e., Nebraska, Kansas, Iowa, and Missouri. Second, different accident prediction models for HRGC crash prediction were compared. These models included the accident prediction and severity (APS) model recommended by the FRA and other commonly used crash prediction models such as general linear regression models with fixed or random effects, zero-inflated models, and hurdle models. The APS model was found the best fit for the HRGC data in Region VII area. Therefore, the APS model was calibrated and validated including the index of hazardous materials released and its impact on the surrounding areas resulting from HRGC crashes. A risk score model was developed to rank the HRGCs. Finally, a prototype HRGC Safety Management System (SMS) was developed. The prototype underwent testing utilizing crash data from Nebraska and was initially implemented specifically for the state of Nebraska. The prototype SMS structure was designed so that it could be adopted by state Departments of Transportation (DOTs) in Region VII and across the United States. This project benefits the quality of information provided to decision-makers and enhances the statewide safety management of HRGCs. In particular, the development of this SMS can assist HRGC managers in being proactive to safety and risk situations at HRGCs.


Citations (12)


... The safety of non-motorists, essentially comprising pedestrians and bicyclists, remains a pressing concern. These vulnerable road users navigate these crossings with distinct challenges, facing heightened risks due to their limited visibility and slower speeds Zhao et al. 2024). ...

Reference:

Pedestrian and Bicyclist Safety at Highway-Rail Grade Crossings
Data Accuracy Matters: Improving Highway-Rail Grade Crossings Crash Predictions through Inventory Verification
  • Citing Article
  • August 2024

Transportation Research Record Journal of the Transportation Research Board

... The rapid rise of social media has transformed how people communicate, share information, and express opinions, leading to increased emotional expression through online text. Platforms like Facebook and Twitter offer unprecedented opportunities to explore public sentiment, providing real-time access to vast amounts of data that capture diverse perspectives and user feedback on specific product features from various geographical locations [22,23]. In China, emerging social media platforms represented by Sina Weibo provide a medium for people to express opinions and exchange information through their unique openness, real-time nature, interactivity, and low entry barriers, significantly influencing the landscape of information dissemination in society at a rapid pace. ...

Sentiments of Rural U.S. Communities on Electric Vehicles and Infrastructure: Insights from Twitter Data

Sustainability

... The technique was applied on 256 vehicle trains, 76 violations, and 180 non-violation events with 80% accuracy. 15 The safety of pedestrian has also necessary nowadays. The violation may also happen due to pedestrian behaviors on the road. ...

Gate violation prediction at highway-rail grade crossings using tree-based ensemble techniques
  • Citing Article
  • January 2023

Journal of Transportation Safety & Security

... Several approaches have been introduced to compute the Value of Travel Time Saved (VTTS) for various road users, including leisure car drivers, business car drivers, truck drivers, etc. 13,36 . In this study, we used the most recent VTTS estimated and published by the U.S. Department of Transportation, which was reported to be equal to $20.17 per hour per vehicle in 2020 37 . ...

Estimating System and Traveler Costs Due to Lane Closures During Construction and Maintenance Operations
  • Citing Technical Report
  • May 2022

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Li Zhao

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[...]

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Eric C. Thompson

... The calibrated parameters performed better than the default ones, although capturing the wide range of behaviors was unsuccessful. Some studies tried to calibrate the simulation model in specific situations such as work zones [21,22], roads with limited sight distance [23], and exact locations with a particular traffic behavior [24]. Table 1 compares some of the recent efforts to calibrate microsimulation models and provides details about them. ...

Calibration and Validation of a Microsimulation Model of Lane Closures on a Two-Lane Highway Work Zone
  • Citing Article
  • September 2022

Transportation Research Record Journal of the Transportation Research Board

... Furthermore, the interdisciplinary examination of range anxiety and WTP for charging has often been sidelined, despite their mutual influencing factors and measurability through questionnaire surveys. Taking a conjoined look at the psychological prospect of EV drivers can shed light on a more comprehensive understanding of EV users' needs and preferences (Chai et al., 2022;Li et al., 2022), thereby assisting in devising a more pragmatic approach to charging infrastructure planning and price policies (Eliasson, 2021). ...

Revealing driver psychophysiological response to emergency braking in distracted driving based on field experiments

Journal of Intelligent and Connected Vehicles

... The calibrated parameters performed better than the default ones, although capturing the wide range of behaviors was unsuccessful. Some studies tried to calibrate the simulation model in specific situations such as work zones [21,22], roads with limited sight distance [23], and exact locations with a particular traffic behavior [24]. Table 1 compares some of the recent efforts to calibrate microsimulation models and provides details about them. ...

Calibration and Validation Methodology for Simulation Models of Intelligent Work Zones
  • Citing Article
  • March 2022

Transportation Research Record Journal of the Transportation Research Board

... [38] The difference is no chargedesignated EV fast lanes from the suburbs to the city-centers. [39][40][41][42][43][44]. Thus, it would be fair to assume if the largest freeways in and out of the suburbs to the city-center in Kuwait, Freeway 30 and Freeway 40 would have a designated fast lane for EV the demand would take a jump. ...

Exploration of the characteristics and trends of electric vehicle crashes: a case study in Norway

European Transport Research Review

... Due to the reliability and practicability requirements of assisted driving systems, more and more researchers use vehicle trajectory data for LC intent recognition (Xu et al., 2019;Pang et al., 2020;Xia et al., 2021;Zhao et al., 2021). For example, Zhao et al. (2021) developed a LC intention prediction model using HMM and naturalistic driving data from Safety Pilot Model Deployment (SPMD) program. ...

Hidden Markov Model of Lane-Changing-Based Car-Following Behavior on Freeways using Naturalistic Driving Data
  • Citing Article
  • May 2021

Transportation Research Record Journal of the Transportation Research Board

... Tufuor et al. 12 estimated travelers' costs due to lane closures caused by maintenance and construction activities. In prior studies, construction and maintenance costs were estimated based on data published several years earlier than the study and sourced from various regions 13,14 . These studies often focused on a single type of barrier, neglecting the impact of road geometry, traffic mix, and economic factors on the lifetime cost of barriers. ...

Calibrating the Highway Capacity Manual Arterial Travel Time Reliability Model

Journal of Transportation Engineering Part A Systems