Impact of COVID-19 travel-restriction policies on road traffic accident patterns with emphasis on cyclists: A case study of New York City

Investigating the temporal dynamics of motor vehicle collision density patterns in urban road networks - A case study of New York

INTRODUCTION

Motor vehicle collisions are a leading source of mortality and injury on urban highways. From a temporal perspective, the determination of a road segment as being collision-prone over time can fluctuate dramatically, making it difficult for transportation agencies to propose traffic interventions. However, there has been limited research to identify and characterize collision-prone road segments with varying collision density patterns over time.

METHOD

This study proposes an identification and characterization framework that profiles collision-prone roads with various collision density variations. We first employ the spatio-temporal network kernel density estimation (STNKDE) method and time-series clustering to identify road segments with different collision density patterns. Next, we characterize collision-prone road segments based on spatio-temporal information, consequences, vehicle types, and contributing factors to collisions. The proposed method is applied to two-year motor vehicle collision records for New York City.

RESULTS

Seven clusters of road segments with different collision density patterns were identified. Road segments frequently determined as collision-prone were primarily found in Lower Manhattan and the center of the Bronx borough. Furthermore, collisions near road segments that exhibit greater collision densities over time result in more fatalities and injuries, many of which are caused by both human and vehicle factors.

CONCLUSIONS

Collision-prone road segments with various collision density patterns over time have distinct differences in the spatio-temporal domain and the collisions that occur on them.

PRACTICAL APPLICATIONS

The proposed method can help policymakers understand how collision-prone road segments change over time, and can serve as a reference for more targeted traffic treatment.

The immediate effects of vision-zero corridor upgrades on pedestrian crashes in New York: A before-and-after spatial point process approach

The long-term effects of the Vision-Zero (VZ) approach in Scandinavia are well documented. In contrast, information regarding the immediate effects of VZ at the starting phase upon gradual implementation is scarce. Taking New York City as the case study, we analyzed both the local and global effects of the Vision-Zero gradual implementation on pedestrian crashes in the early stage of implementation starting from 2014. The data analysis comprised 8,165 pedestrian injury crashes. Using location data, the crashes were matched to VZ infrastructure improvement location, start and completion dates. The experimental design included a treatment and two types of control conditions, and we controlled for well-known covariates including traffic exposure, land use, and risk-prone areas. We estimated a Geyer Saturation model and kernel density function for modeling the effect of Vision-Zero on crash intensity and dispersion two years before and after the implementation of Vision-Zero. The results reveal a significant global decrease of 6.1 % (p = 0.004) in pedestrian crash incidence in the treated sections compared with the control group two years after the treatment, and a greater dispersion of pedestrian injuries following the policy implementation.

Vulnerable road user injury trends following the COVID-19 pandemic in Toronto, Canada: An interrupted time series analysis

BACKGROUND

The COVID-19 pandemic altered traffic patterns worldwide, potentially impacting pedestrian and bicyclists safety in urban areas. In Toronto, Canada, work from home policies, bicycle network expansion, and quiet streets were implemented to support walking and cycling. We examined pedestrian and bicyclist injury trends from 2012 to 2022, utilizing police-reported killed or severely injured (KSI), emergency department (ED) visits and hospitalization data.

METHODS

We used an interrupted time series design, with injury counts aggregated quarterly. We fit a negative binomial regression using a Bayesian modeling approach to data prior to the pandemic that included a secular time trend, quarterly seasonal indicator variables, and autoregressive terms. The differences between observed and expected injury counts based on pre-pandemic trends with 95% credible intervals (CIs) were computed.

RESULTS

There were 38% fewer pedestrian KSI (95%CI: 19%, 52%), 35% fewer ED visits (95%CI: 28%, 42%), and 19% fewer hospitalizations (95%CI: 2%, 32%) since the beginning of the COVID-19 pandemic. A reduction of 35% (95%CI: 7%, 54%) in KSI bicyclist injuries was observed, but However, ED visits and hospitalizations from bicycle-motor vehicle collisions were compatible with pre-pandemic trends. In contrast, for bicycle injuries not involving motor vehicles, large increases were observed for both ED visits, 73% (95% CI: 49%, 103%) and for hospitalization 108% (95% CI: 38%, 208%).

CONCLUSION

New road safety interventions during the pandemic may have improved road safety for vulnerable road users with respect to collisions with motor vehicles; however, further investigation into the risk factors for bicycle injuries not involving motor vehicles is required.

An ALNS-based approach for the traffic-police-routine-patrol-vehicle assignment problem in resource allocation analysis of traffic crashes

OBJECTIVES

Imbalances between limited police resource allocations and the timely handling of road traffic crashes are prevalent. To optimize resource allocations and route choices for traffic police routine patrol vehicle (RPV) assignments, a dynamic crash handling response model was developed.

METHODS

This approach was characterized by two objective functions: the minimum waiting time and the minimum number of RPVs. In particular, an adaptive large neighborhood search (ALNS) was designed to solve the model. Then, the proposed ALNS-based approach was examined using comprehensive traffic and crash data from Ningbo, China.

RESULTS

Finally, a sensitivity analysis was conducted to evaluate the bi-objective of the proposed model and simultaneously demonstrate the efficiency of the obtained solutions. Two resolution methods, the global static resolution mode, and real-time dynamic resolution mode, were applied to explore the optimal solution.

CONCLUSIONS

The results show that the optimal allocation scheme for traffic police is 13 RPVs based on the global static resolution mode. Specifically, the average waiting time for traffic crash handling can be reduced to 5.5 min, with 53.8% less than 5.0 min and 90.0% less than 10.0 min.

The Impact of the COVID-19 Shelter-in-Place Order on Traumatic Brain Injuries in San Francisco, California

BACKGROUND

In response to the COVID-19 pandemic, San Francisco, California issued a shelter-in-place (SIP) order in March 2020, during which emergency physicians noted a drop in trauma cases, as well as a change in traditional mechanisms of trauma.

OBJECTIVES

Our objective was to determine the epidemiology of traumatic brain injury (TBI) pre- and post-COVID-19 SIP.

METHODS

We reviewed the electronic medical record of the only trauma center in the city of San Francisco, to determine the number of and characteristics of patients with a diagnosis of head injury presenting to the emergency department between December 16, 2019 and June 16, 2020. Using chi-squared and Fisher's exact tests when appropriate, we compared pre- and post- COVID-19 lockdown epidemiology.

RESULTS

There were 1246 TBI-related visits during the 6-month study period. Bi-weekly TBI cases decreased by 36.64% 2 weeks after the COVID-19 SIP and then increased to near baseline levels by June 2020. TBI patients during SIP were older (mean age: 53.3 years pre-SIP vs. 58.2 post-SIP; p < 0.001), more likely to be male (odds ratio 1.43, 95% confidence interval 1.14-1.81), and less likely to be 17 or younger (8.9% vs. 0.5%, pre- to post-SIP respectively, p = 0.003). Patients were less likely to be Hispanic (27.2% vs. 21.7% pre- to post-SIP, respectively, p = 0.029). The proportion of TBI visits attributable to cycling accidents increased (14.1% to 52.7%, p < 0.001), whereas those attributable to pedestrians involved in road traffic accidents decreased (37.2% to 12.7%, p = 0.003).

CONCLUSIONS

Understanding the changing epidemiology of TBI during the COVID-19 pandemic can aid in immediate and future disaster resource planning.

The Impact of The COVID-19 Pandemic on Hospital Admissions Due to Road Traffic Crashes; a Systematic Review and Meta-Analysis

Introduction

During the unprecedented COVID-19 lockdowns, road traffic was limited, and a change in the traumatic emergency admission pattern was anticipated. We conducted the current systematic review and meta-analysis to assess the impact of the COVID-19 pandemic on hospital admissions due to road traffic crashes.

Methods

This systematic review and meta-analysis was conducted based on the Joanna Briggs Institute (JBI) instructions. The following databases were searched: PubMed, ISI Web of Knowledge, Scopus, ProQuest, and the Cochrane Library. Two independent reviewers screened articles based on the inclusion criteria for the review and assessed the methodological quality of the included studies using an appropriate appraisal checklist, introduced by the JBI, based on the study type. The meta-analysis was performed using Comprehensive meta-analysis (CMA) software. Considering the heterogeneity among studies, a random effect model was adopted to estimate the pooled effect with 95% confidence interval (CI) for binary outcomes.

Results

A total of 13 studies were included in this systematic review, and all of them were considered for meta-analysis. According to the meta-analysis, differences in hospital admission rates during the COVID-19 pandemic and one year before this pandemic were statistically significant [RR: 0.685 CI 95% (0.578 -0.813) p<0.00001]. The heterogeneity assessment of the included studies in the meta-analysis showed high heterogeneity (I2=78%, p<0.00001).

Conclusion

The results of this systematic review showed that the COVID-19 pandemic dramatically reduced the number of hospital admissions related to road traffic crashes because of both quarantines and lifestyle changes. Health policymakers and top health managers might use the results of this systematic review in similar contexts in the future.

Assessing the Effect of COVID-19 on the Traffic Safety of Intercity and Major Intracity Roads in Saudi Arabia

The coronavirus disease 2019 (COVID-19) pandemic led to rapid and unexpected changes across the world, particularly in road safety. Thus, this work assesses the impact of COVID-19 accompanied by government preventive policies on road safety in Saudi Arabia by investigating the crash frequency and crash rates. A 4-year crash dataset relating to 2018-2021 was collected, covering about 71,000 km in total road length. It covers all intercity roads and some of the major intercity roads in Saudi Arabia with over 40,000 data logs of involved crashes. We considered three different time phases to observe road safety. These time phases were identified by the duration of government curfew measures against COVID-19 (before, during, and after). The crash frequency analysis showed that the curfew during COVID-19 significantly impacted the crash reduction. At a national level, the crash frequency decreased during 2020 and reached a 33.2% reduction compared to 2019 (the previous year), and it surprisingly continued decreasing in 2021 (the consequent year) to another 37.7% reduction although the government measures were lifted. Moreover, considering the traffic volume and road geometry, we analyzed crash rates for 36 selected segments, and the results showed a significant reduction in the crash rate before and after the COVID-19 pandemic. Additionally, a random effect negative binomial model was developed to quantify the impact of the COVID-19 pandemic. The results showed that the reduction in crashes was significant during and after COVID-19. Also, single roads (two-lane, two-way) were found to be more dangerous than other types of roads.

Road crashes in Adelaide metropolitan region, the consequences of COVID-19

Background

Many countries instituted lockdown rules as the COVID-19 pandemic progressed, however, the effects of COVID-19 on transportation safety vary widely across countries and regions. In several situations, it has been shown that although the COVID-19 closure has decreased average traffic flow, it has also led to an increase in speeding, which will indeed increase the severity of crashes and the number of fatalities and serious injuries.

Methods

At the local level, Generalized linear Mixed (GLM) modelling is used to look at how often road crashes changed in the Adelaide metropolitan area before and after the COVID-19 pandemic. The Geographically Weighted Generalized Linear Model (GWGLM) is also used to explore how the association between the number of crashes and the factors that explain them varies across census blocks. Using both no-spatial and spatial models, the effects of urban structure elements like land use mix, road network design, distance to CBD, and proximity to public transit on the frequency of crashes at the local level were studied.

Results

This research showed that lockdown orders led to a mild reduction (approximately 7%) in crash frequency. However, this decrease, which has occurred mostly during the first three months of the lockdown, has not systematically alleviated traffic safety risks in the Greater Adelaide Metropolitan Area. Crash hotspots shifted from areas adjacent to workplaces and education centres to green spaces and city fringes, while crash incidence periods switched from weekdays to weekends and winter to summer.

Implications

The outcomes of this research provided insights into the impact of shifting driving behaviour on safety during disorderly catastrophes such as COVID-19.

A knowledge elicitation approach to traffic accident analysis in open data: comparing periods before and after the Covid-19 outbreak

Extracting knowledge from open data of traffic accidents has been attracting increasing attention to policymakers responsible for road safety. This article presents a knowledge elicitation approach to exploring the determinants of traffic accidents from open government data of an urban area in Taiwan. The collected open dataset contains 34 decisional attributes and one predictive attribute (i.e., type of injury, including head, breast, leg), and 47,974 cases. Prediction models using a classification-oriented mechanism and generated rules that considered datasets from before (B-dataset; 30,116 cases) and after (A-dataset; 17,868 cases) beginning to combat the Covid-19 pandemic in an urban area of Taiwan were compared. The findings showed that prediction accuracy was acceptable but not high, at 70.73% for B-dataset and 74.77% for A-dataset. Determinants in the human and vehicle categories revealed higher classification ranks than those in the temporal and environment categories. Traffic accidents involving motorcycles were 5.13% higher in A-dataset, whereas those involving cars were 4.11% lower. Injury on leg or foot was 3.46% higher in A-dataset, whereas other types of injury were up to 1.00% lower. The average support for rules in the A-dataset rule base and the simplicity of the A-dataset decision tree were higher than those of B-dataset. The research demonstrates the value of open government data in prediction model development and knowledge elicitation to support policymaking in the traffic safety domain.

Impacts of COVID-19 Travel Restriction Policies on the Traffic Quality of the National and Provincial Trunk Highway Network: A Case Study of Shaanxi Province

According to recent research, the COVID-19 pandemic has impacted road traffic quality. This study aims to analyze the impacts of COVID-19 travel restriction policies on the traffic quality of the national and provincial trunk highway network (NPTHN) in Shaanxi Province. We collected the traffic data of the NPTHN for three consecutive years (from 2019 to 2021), before and after the COVID-19 outbreak, including weekly average daily traffic, weekly traffic interruption times, weekly traffic control time, weekly traffic accidents, weekly traffic injuries, and weekly traffic deaths. Using descriptive statistics and dynamic analysis methods, we studied the safety and service levels of the NPTHN. We set up an assessment model of the NPTHN operational orderliness through dissipative structure theory and entropy theory to study the operational orderliness of the NPTHN. Results show that in 2020, the service level, safety level, and operational orderliness of the NPTHN dropped to the lowest levels. The pandemic was gradually brought under control, and the travel restriction policies were gradually reduced and lifted. The adverse impacts on the operational orderliness of the NPTHN decreased, but the operational orderliness did not yet recover to the pre-pandemic level. Meanwhile, the service and safety levels of the NPTHN did not recover. Taken together, the COVID-19 travel restriction policies had adverse impacts on the traffic quality of the NPTHN in Shaanxi Province.