Transportation agencies post and enforce reduced speed limits in work zones to ensure work zone safety, since traffic speed is found to be associated with work zone crash risks. However, prior findings on the relationship between speed and crash rate in work zones are inconsistent. This may be attributed to the methods of statistical associations between traffic speed and crash risks that do not necessarily discover true causal relations. In fact, work zone presence could lead to the reduction of actual traffic speed that influences crash risks, where it may also directly impose effects on crash risks as a result of work zone configurations. The actual traffic speed (not posted speed limit) is also known as a “mediator” where work zones can indirectly impact the crash risks. It is challenging to rigorously separate the causal effect of traffic speed on work zone crash risk from that directly caused by work zones. The underlying causal relation could help to determine what reduced post speed limit (with enforcement) is necessary to ensure work zone safety under the most desired “actual traffic speed”. This study proposes to use the sequential g-estimation and the regression discontinuity design to estimate the controlled direct effect of traffic speed on work zone crashes. Two research gaps are identified and filled: inaccurate inferences of the effect of reduced speed limit in work zones as a result of ignoring (1) potential post-treatment bias since traffic speed is a mediator; and (2) potential confounding bias caused by unobservable roadway characteristics. The proposed methodology was applied to 4008 work zones in Pennsylvania from 2015 to 2017, and the results were validated through a series of robustness tests. The results indicate that the direct causal effect of the presence of work zones on crash risk is significantly positive when the traffic speed is relatively low (i.e., lower than 55 mph in this case study), while traffic speed has a positive causal effect on crash occurrences when the actual traffic speed is high (i.e., greater or equal to 55 mph). It suggests that strictly enforcing reduced posted speed limits in work zones is particularly effective when the actual traffic speed is greater than 55 mph. This is particularly true on roadways with high traffic volume (i.e., more than 20,000 vehicles per day), long, and daytime work zones (i.e., more than 3000 m). On the other hand, the effect of enforcing reduced speed on work zone safety is unclear when the actual speed is already low. In this case, improving work zone configurations and driving behaviors may be more effective in reducing crash risks..
@article{zhang2023speed,title={How effective is reducing traffic speed for safer work zones? Methodology and a case study in Pennsylvania},author={Zhang, Zhuoran and Akinci, Burcu and Qian, Sean},journal={Accident Analysis & Prevention},volume={183},pages={106966},year={2023},html={https://doi.org/10.1016/j.aap.2023.106966},publisher={Elsevier},abbr={AAP},code={https://github.com/Andyzr/work_zone_safety},bibtex_show={true}}
2022
AMAR
Inferring the causal effect of work zones on crashes: Methodology and a case study
The increasing number of crashes occurring in work zones has received considerable attention in recent years. Previous studies have mainly focused on associations between work zone configurations and crash occurrence. Although identification of associational relations helps us understand how work zones co-exist with crashes, it does not provide interventional guidelines necessary to improve safety of work zone operations. In this paper, a causal inference model based on the potential outcome framework is proposed to rigorously infer the causal effects of work zone presence on crash risks under various work zone configurations, along with robustness tests. In developing such a causal model, three research gaps are identified and addressed: (1) potential confounding bias due to unobservable roadway characteristics; (2) potential bias caused by unobserved variables in multisource data; and (3) lack of actually observed traffic data and weather information at the exact time when a crash occurred and lack of large-scale high-granular data. The proposed methodology is applied to 5,006 work zones in Pennsylvania from 2015 to 2017, and the results are validated via a series of robustness tests. The results show that the causal effect of a work zone on crash occurrence is significantly positive, especially on roadways with high traffic volumes, on long-distance work zones, and work zones conducted during daytime. It appears that conducting work zones during nighttime with the current deployment strategies on Pennsylvania state roads does not necessarily increase crash risks, but a work zone significantly increases crash risks during day time.
@article{zhang2022inferring,title={Inferring the causal effect of work zones on crashes: Methodology and a case study},author={Zhang, Zhuoran and Akinci, Burcu and Qian, Sean},journal={Analytic Methods in Accident Research},volume={33},pages={100203},year={2022},html={https://doi.org/10.1016/j.amar.2021.100203},publisher={Elsevier},abbr={AMAR},code={https://github.com/Andyzr/work_zone_safety},poster={https://www.zhangzr.net/assets/pdf/trb-poster-wzsafety.pdf},bibtex_show={true}}
AAP
Inferring heterogeneous treatment effects of work zones on crashes
The increasing number of work zone crashes has been a significant concern for road users, transportation agencies, and researchers. Crashes can be caused by work zones, and this effect changes across different work zone configurations, traffic volumes, roadway functional classifications, and weather conditions. This is typically represented by Crash Modification Functions (CMFunctions). However, current methods for developing work zone CMFunctions have two major limitations: (1) They focus on analyzing statistical associations and fail to mitigate the confounding bias due to possible unobservable roadway characteristics; and (2) They cannot address CMFunctions of multiple variables simultaneously, such as weather and traffic conditions, since they are represented using mixed data types (continuous and categorical) that could potentially affect the causal effect of work zones on crashes. In this study, we develop a method that utilizes causal forest with fixed-effect modeling to mitigate the confounding bias while identifying CMFunctions conditioning on various environmental characteristics, including work zone configurations, traffic volume, roadway functional classification, and weather conditions. The developed method was applied to 3378 work zones that occurred in Pennsylvania between 2015 and 2017. The results were validated via a series of robustness tests. The validations demonstrate that this method can mitigate the confounding bias and identify CMFunctions of multiple variables. The results also show that the causal effect of a work zone on crash occurrence is significantly positive (p smaller than 0.05) on roadways with high traffic volumes (e.g., larger than 20,000 vehicles per day) and on medium length (e.g., 2000 to 5000 m) work zones. It appears that having medium-long (e.g., between 6000 and 8000 m) work zones or long duration (e.g., longer than 4 h) work zones do not necessarily lead to extra crashes.
@article{zhang2022inferrinh,title={Inferring heterogeneous treatment effects of work zones on crashes},author={Zhang, Zhuoran and Akinci, Burcu and Qian, Sean},journal={Accident Analysis & Prevention},volume={177},pages={106811},year={2022},html={https://doi.org/10.1016/j.aap.2022.106811},publisher={Elsevier},abbr={AAP},bibtex_show={true}}
CRC
A Novel Map-Matching Algorithm for Relating Work Zones and Crashes
@inproceedings{zhang2022novel,title={A Novel Map-Matching Algorithm for Relating Work Zones and Crashes},author={Zhang, Zhuoran and Akinci, Burcu and Qian, Sean},booktitle={Construction Research Congress},pages={366--375},html={https://doi.org/10.1061/9780784483985.037},year={2022},abbr={CRC},bibtex_show={true}}
i3ce
Identifying Temporal Instability in Factors Causing Work Zone Crash Occurrences Using Fast Causal Inference
@inproceedings{zhang2021identifying,title={Identifying Temporal Instability in Factors Causing Work Zone Crash Occurrences Using Fast Causal Inference},author={Zhang, Zhuoran and Akinci, Burcu and Qian, Sean},booktitle={Computing in Civil Engineering},html={https://doi.org/10.1061/9780784483893.013},pages={98--105},year={2022},abbr={i3ce},bibtex_show={true}}
2018
CRIOCM
Finding Academic Concerns on Real Estate of US and China: A Topic Modeling Based Exploration
Zhang, Zhuoran,
Qiang, Maoshan,
and Jiang, Hanchen
In Proceedings of the 21st International Symposium on Advancement of Construction Management and Real Estate
2018
@inproceedings{zhang2018finding,title={Finding Academic Concerns on Real Estate of US and China: A Topic Modeling Based Exploration},author={Zhang, Zhuoran and Qiang, Maoshan and Jiang, Hanchen},booktitle={Proceedings of the 21st International Symposium on Advancement of Construction Management and Real Estate},pages={807--817},year={2018},html={https://link.springer.com/chapter/10.1007/978-981-10-6190-5_73},organization={Springer},abbr={CRIOCM},bibtex_show={true}}