1
|
Kim D, Park K. Analysis of potential collisions between pedestrians and personal transportation devices in a university campus: an application of unmanned aerial vehicles. JOURNAL OF AMERICAN COLLEGE HEALTH : J OF ACH 2023; 71:2272-2279. [PMID: 34516938 DOI: 10.1080/07448481.2021.1967358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 06/10/2021] [Accepted: 08/06/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVE To identify factors that contribute to near-miss collisions between pedestrians and personal transportation devices (PTDs) in a university campus using a novel data collection method, unmanned aerial vehicle (UAV). PARTICIPANTS A total of 3,349 pedestrians and 173 PTD riders were detected through UAV observations. METHODS The researchers employed UAV technology to capture and geocode the interactions and behavior of pedestrians and PTD riders. Then, a multilevel logistic regression model examined factors that contribute to near-miss collisions between pedestrians and PTDs. RESULTS The model outputs indicate that higher speed, non-bicycle PTDs (eg, skateboard and scooter), and some preventive actions, like reducing speed, deviating, and weaving, increase the probability of a PTD rider getting involved in a near-miss collision. CONCLUSIONS Findings can guide campus planners to redesign the streets as a safe environment for all transportation modes and implement appropriate regulations and education programs, especially for non-bicycle PTD riders.
Collapse
Affiliation(s)
- Dohyung Kim
- Department of Urban and Regional Planning, California State Polytechnic University, Pomona, California, USA
| | - Keunhyun Park
- Department of Landscape Architecture and Environmental Planning, Utah State University, Logan, Utah, USA
| |
Collapse
|
2
|
Loo BPY, Fan Z, Lian T, Zhang F. Using computer vision and machine learning to identify bus safety risk factors. ACCIDENT; ANALYSIS AND PREVENTION 2023; 185:107017. [PMID: 36889236 DOI: 10.1016/j.aap.2023.107017] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 02/11/2023] [Accepted: 02/27/2023] [Indexed: 06/18/2023]
Abstract
In road safety research, bus crashes are particularly noteworthy because of the large number of bus passengers involved and the challenge that it puts to the road network (with the closure of multiple lanes or entire roads for hours) and the public health care system (with multiple injuries that need to be dispatched to public hospitals within a short time). The significance of improving bus safety is high in cities heavily relying on buses as a major means of public transport. The recent paradigm shifts of road design from primarily vehicle-oriented to people-oriented urge us to examine street and pedestrian behavioural factors more closely. Notably, the street environment is highly dynamic, corresponding to different times of the day. To fill this research gap, this study leverages a rich dataset - video data from bus dashcam footage - to identify some high-risk factors for estimating the frequency of bus crashes. This research applies deep learning models and computer vision techniques and constructs a series of behavioural and street factors: pedestrian exposure factors, pedestrian jaywalking, bus stop crowding, sidewalk railing, and sharp turning locations. Important risk factors are identified, and future planning interventions are suggested. In particular, road safety administrations need to devote more efforts to improve bus safety along streets with a high volume of pedestrians, recognise the importance of protection railing in protecting pedestrians during serious bus crashes, and take measures to ease bus stop crowding to prevent slight bus injuries.
Collapse
Affiliation(s)
- Becky P Y Loo
- Department of Geography, The University of Hong Kong, Hong Kong, China; School of Geography and Environment, Jiangxi Normal University, Nanchang, China.
| | - Zhuangyuan Fan
- Department of Geography, The University of Hong Kong, Hong Kong, China.
| | - Ting Lian
- Department of Geography, The University of Hong Kong, Hong Kong, China.
| | - Feiyang Zhang
- Department of Geography, The University of Hong Kong, Hong Kong, China
| |
Collapse
|
3
|
Xing J, Zhang Q, Cheng Q, Zu Z. A Geographical and Temporal Risk Evaluation Method for Red-Light Violations by Pedestrians at Signalized Intersections: Analysis and Results of Suzhou, China. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:14420. [PMID: 36361298 PMCID: PMC9654891 DOI: 10.3390/ijerph192114420] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/29/2022] [Accepted: 11/02/2022] [Indexed: 06/16/2023]
Abstract
Red-light violations of pedestrians crossing at signal intersections is one of the key factors in pedestrian traffic accidents. Even though there are various studies on pedestrian behavior and pedestrian traffic conflicts, few focus on the risk of different crosswalks for the violating pedestrian group. Due to the spatio-temporal nature of violation risk, this study proposes a geographical and temporal risk evaluation method for pedestrian red-light violations, which combines actual survey and video acquisition. First, in the geographical-based risk evaluation, the pedestrian violation rate at signal intersections is investigated by Pearson correlation analysis to extract the significant influencing factors from traffic conditions, built environment, and crosswalk facilities. Second, in the temporal-based risk evaluation, the survival analysis method is developed to quantify the risk of pedestrian violation in different scenarios as time passes by. Finally, this study selects 16 typical signalized intersections in Suzhou, China, with 881 pedestrian crosswalk violations from a total size of 4586 pedestrians as survey cases. Results indicate that crossing distance, traffic volume on the crosswalk, red-light time, and crosswalk-type variables all contribute to the effect of pedestrian violation from a geographical perspective, and the installation of waiting refuge islands has the most significant impact. From the temporal perspective, the increases in red-light time, number of lanes, and traffic volume have a mitigating effect on the violations with pedestrian waiting time increases. This study aims to provide a development-oriented path by proposing an analytical framework that reconsiders geographical and temporal risk factors of violation. The findings could help transport planners understand the effect of pedestrian violation-related traffic risk and develop operational measures and crosswalk design schemes for controlling pedestrian violations occurring in local communities.
Collapse
Affiliation(s)
- Jiping Xing
- School of Transportation, Southeast University, Nanjing 211189, China
| | - Qi Zhang
- School of Transportation, Southeast University, Nanjing 211189, China
| | - Qixiu Cheng
- Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China
| | - Zhenshan Zu
- Traffic Management Department of Suzhou Wujiang District Public Security Bureau, Suzhou 215299, China
| |
Collapse
|
4
|
Matsuo K, Miyazaki K, Sugiki N. A Method for Locational Risk Estimation of Vehicle-Children Accidents Considering Children's Travel Purposes. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:14123. [PMID: 36361002 PMCID: PMC9655445 DOI: 10.3390/ijerph192114123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/26/2022] [Accepted: 10/27/2022] [Indexed: 06/16/2023]
Abstract
The reduction in locational traffic accident risks through appropriate traffic safety management is important to support, maintain, and improve children's safe and independent mobility. This study proposes and verifies a method to evaluate the risk of elementary school students-vehicle accidents (ESSVAs) at individual intersections on residential roads in Toyohashi city, Japan, considering the difference in travel purposes (i.e., school commuting purpose; SCP or non-school commuting purpose: NSCP), based on a statistical regression model and Empirical Bayes (EB) estimation. The results showed that the ESSVA risk of children's travel in SCP is lower than that in NSCP, and not only ESSVAs in SCP but also most ESSVAs in NSCP occurred on or near the designated school routes. Therefore, it would make sense to implement traffic safety management and measures focusing on school routes. It was also found that the locational ESSVA risk structure is different depending on whether the purpose of the children's travels is SCP or NSCP in the statistical model. Finally, it was suggested that evaluation of locational ESSVA risks based on the EB estimation is useful for efficiently extracting locations where traffic safety measures should be implemented compared to that only based on the number of accidents in the past.
Collapse
Affiliation(s)
- Kojiro Matsuo
- Department of Architecture and Civil Engineering, Toyohashi University of Technology, Toyohashi 441-8580, Japan
| | - Kosuke Miyazaki
- Department of Civil Engineering, National Institute of Technology, Kagawa College, Kagawa 761-8058, Japan
| | - Nao Sugiki
- Department of Architecture and Civil Engineering, Toyohashi University of Technology, Toyohashi 441-8580, Japan
| |
Collapse
|
5
|
Xu P, Bai L, Pei X, Wong SC, Zhou H. Uncertainty matters: Bayesian modeling of bicycle crashes with incomplete exposure data. ACCIDENT; ANALYSIS AND PREVENTION 2022; 165:106518. [PMID: 34894484 DOI: 10.1016/j.aap.2021.106518] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 10/08/2021] [Accepted: 11/29/2021] [Indexed: 06/14/2023]
Abstract
BACKGROUND One major challenge faced by neighborhood-level bicycle safety analysis is the lack of complete and reliable exposure data for the entire area under investigation. Although the conventional travel-diary surveys, together with the emerging smartphone fitness applications and bike-sharing systems, provide straightforward and valuable opportunities to estimate territory-wide bicycle activities, the obtained ridership suffers inherently from underreporting. METHODS We introduced the Bayesian simultaneous-equation model as a sound methodological alternative here to address the uncertainty arising from incomplete exposure data when modeling bicycle crashes. The proposed method was successfully fitted to a crowdsourced dataset of 792 bicycle-motor vehicle (BMV) crashes aggregated from 209 neighborhoods over a 3-year period in Hong Kong. RESULTS Our analysis empirically demonstrated the bias due to omission of activity-based exposure measures or to the direct use of cycling distance extracted from the travel-diary survey without correcting for incompleteness. By modeling bicycle activities and the frequency of BMV crashes simultaneously, we also provided new evidence that an expansion of bicycle infrastructure was likely associated with a significant increase in cycling levels and a substantial reduction in the risk of BMV crashes, despite a slight increase in the absolute number of BMV crashes. CONCLUSIONS Our approach is promising in adjusting for the uncertainty in raw exposure data, extrapolating the missing exposure values, and untangling the linkage among built environment, bicycle activities, and the frequency of BMV crashes within a unified framework. To promote safer cycling, designated facilities should be provided to consecutively separate cyclists from motor vehicles.
Collapse
Affiliation(s)
- Pengpeng Xu
- School of Civil Engineering and Transportation, South China University of Technology, Guangzhou, China; Department of Civil Engineering, The University of Hong Kong, Hong Kong, China
| | - Lu Bai
- Department of Civil Engineering, The University of Hong Kong, Hong Kong, China
| | - Xin Pei
- Department of Automation, Tsinghua University, Beijing, China
| | - S C Wong
- Department of Civil Engineering, The University of Hong Kong, Hong Kong, China; Guangdong - Hong Kong - Macau Joint Laboratory for Smart Cities, Hong Kong, China
| | - Hanchu Zhou
- School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan, China; School of Data Science, City University of Hong Kong, Hong Kong, China.
| |
Collapse
|
6
|
Lv M, Wang N, Yao S, Wu J, Fang L. Towards Healthy Aging: Influence of the Built Environment on Elderly Pedestrian Safety at the Micro-Level. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph18189534. [PMID: 34574478 PMCID: PMC8470885 DOI: 10.3390/ijerph18189534] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 08/27/2021] [Accepted: 09/08/2021] [Indexed: 12/23/2022]
Abstract
As vulnerable road users, elderly pedestrians are more likely to be injured in road crashes due to declining physical and perceptual capabilities. Most previous studies on the influence of the built environment on elderly pedestrian safety focused on intersections or areal units. Using a district of Shanghai as the study area, this research investigated the effects of the built environment at the road segment level with elderly pedestrian collision, taxi tracking point, point of interest, street view image, open street map, land use, housing price, and elderly population datasets. In particular, this research employed both Poisson and geographically weighted Poisson regression (GWPR) models to account for spatial nonstationarity. The Poisson model indicates that green space, sidewalks, and junctions on the roads significantly affected elderly pedestrian safety, and roads around nursing homes, schools, bus stops, metro stations, traditional markets, and supermarkets were hazardous for elderly pedestrians. The results of the GWPR model suggest that the influence of factors varied across the study area. Green space could decrease the risk of elderly pedestrian collisions only in areas without congested environments. Separations need to be installed between roadways and sidewalks to improve elderly road safety.
Collapse
Affiliation(s)
- Muhan Lv
- Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China; (M.L.); (N.W.); (J.W.)
- School of Geographic Sciences, East China Normal University, Shanghai 200241, China
| | - Ningcheng Wang
- Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China; (M.L.); (N.W.); (J.W.)
- School of Geographic Sciences, East China Normal University, Shanghai 200241, China
| | - Shenjun Yao
- Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China; (M.L.); (N.W.); (J.W.)
- School of Geographic Sciences, East China Normal University, Shanghai 200241, China
- Research Center for China Administrative Division, East China Normal University, Shanghai 200241, China
- Correspondence: ; Tel.: +86-21-5434-1204
| | - Jianping Wu
- Key Laboratory of Geographic Information Science (Ministry of Education), East China Normal University, Shanghai 200241, China; (M.L.); (N.W.); (J.W.)
- School of Geographic Sciences, East China Normal University, Shanghai 200241, China
| | - Lei Fang
- Department of Environmental Science and Engineering, Fudan University, Shanghai 200438, China;
| |
Collapse
|
7
|
Ji S, Wang Y, Wang Y. Geographically weighted poisson regression under linear model of coregionalization assistance: Application to a bicycle crash study. ACCIDENT; ANALYSIS AND PREVENTION 2021; 159:106230. [PMID: 34153640 DOI: 10.1016/j.aap.2021.106230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2020] [Revised: 04/27/2021] [Accepted: 05/31/2021] [Indexed: 06/13/2023]
Abstract
While cycling benefits individuals and society, cyclists are vulnerable road users, and their safety concerns arouse more macro-level spatial crash studies. Our study intends to investigate the spatial effects of population, land use, and bicycle lane infrastructures on bicycle crashes. This was done by developing a semi-parametric Geographically Weighted Poisson Regression (sGWPR) model which deals with the issue of spatial correlation and spatial non-stationarity simultaneously. It is a model that combines both constant and geographically varying parameters. To determine which parameter is fixed or non-stationary, previous studies suggest monitoring the Akaike Information Criterion (AICc). Yet, relying only on AICc might bury some spatial associations. So, in this study, we propose a Linear Model of Coregionalization (LMC) to assist the decision. Here, we use bicycle crash data across the metropolitan area of Greater Melbourne to establish sGWPR models suggested by AICc and LMC, respectively. Comparing the two sGWPR models, we found the sGWPR model under LMC results performs as well as sGWPR models suggested by AICc from the AICc perspective, and a 22.5% improvement in the mean squared error (MSE). It also uncovers more details about the spatial relationship between bicycle crashes and bicycle lane intersection density (BLID), an effect not suggested under AICc results. The parameters of BLID, a new measurement of bicycle lane facilities proposed by us, vary over space across analysis zones in Greater Melbourne.
Collapse
Affiliation(s)
- Shujuan Ji
- Key Laboratory of Transport Industry of Management, Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area, Chang'an University, P.O. Box 487, Xi'an 710064, China
| | - Yuanqing Wang
- Key Laboratory of Transport Industry of Management, Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area, Chang'an University, P.O. Box 487, Xi'an 710064, China.
| | - Yao Wang
- Key Laboratory of Transport Industry of Management, Control and Cycle Repair Technology for Traffic Network Facilities in Ecological Security Barrier Area, Chang'an University, P.O. Box 487, Xi'an 710064, China
| |
Collapse
|
8
|
Yasmin S, Bhowmik T, Rahman M, Eluru N. Enhancing non-motorist safety by simulating trip exposure using a transportation planning approach. ACCIDENT; ANALYSIS AND PREVENTION 2021; 156:106128. [PMID: 33915343 DOI: 10.1016/j.aap.2021.106128] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 03/23/2021] [Accepted: 04/05/2021] [Indexed: 06/12/2023]
Abstract
Traditionally, in developing non-motorized crash prediction models, safety researchers have employed land use and urban form variables as surrogate for exposure information (such as pedestrian, bicyclist volumes and vehicular traffic). The quality of these crash prediction models is affected by the lack of "true" non-motorized exposure data. High-resolution modeling frameworks such as activity-based or trip-based approach could be pursued for evaluating planning level non-motorist demand. However, running a travel demand model system to generate demand inputs for non-motorized safety is cumbersome and resource intensive. The current study is focused on addressing this drawback by developing an integrated non-motorized demand and crash prediction framework for mobility and safety analysis. Towards this end, we propose a three-step framework to evaluate non-motorists safety: (1) develop aggregate level models for non-motorist generation and attraction at a zonal level, (2) develop non-motorists trip exposure matrices for safety evaluation and (3) develop aggregate level non-motorists crash frequency and severity proportion models. The framework is developed for the Central Florida region using non-motorist demand data from National Household Travel Survey (2009) Florida Add-on and non-motorist crash frequency and severity data from Florida. The applicability of the framework is illustrated through extensive policy scenario analysis.
Collapse
Affiliation(s)
- Shamsunnahar Yasmin
- Queensland University of Technology (QUT), Centre for Accident Research & Road Safety - Queensland (CARRS-Q), Australia & Research Affiliate, Department of Civil, Environmental & Construction Engineering, University of Central Florida, USA.
| | - Tanmoy Bhowmik
- Department of Civil, Environmental & Construction Engineering, University of Central Florida, USA.
| | | | - Naveen Eluru
- Department of Civil, Environmental & Construction Engineering, University of Central Florida, USA.
| |
Collapse
|
9
|
Loo BPY, Tsoi KH, Wong PPY, Lai PC. Identification of superspreading environment under COVID-19 through human mobility data. Sci Rep 2021; 11:4699. [PMID: 33633273 PMCID: PMC7907097 DOI: 10.1038/s41598-021-84089-w] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2020] [Accepted: 02/09/2021] [Indexed: 01/13/2023] Open
Abstract
COVID-19 reaffirms the vital role of superspreaders in a pandemic. We propose to broaden the research on superspreaders through integrating human mobility data and geographical factors to identify superspreading environment. Six types of popular public facilities were selected: bars, shopping centres, karaoke/cinemas, mega shopping malls, public libraries, and sports centres. A historical dataset on mobility was used to calculate the generalized activity space and space-time prism of individuals during a pre-pandemic period. Analysis of geographic interconnections of public facilities yielded locations by different classes of potential spatial risk. These risk surfaces were weighed and integrated into a "risk map of superspreading environment" (SE-risk map) at the city level. Overall, the proposed method can estimate empirical hot spots of superspreading environment with statistical accuracy. The SE-risk map of Hong Kong can pre-identify areas that overlap with the actual disease clusters of bar-related transmission. Our study presents first-of-its-kind research that combines data on facility location and human mobility to identify superspreading environment. The resultant SE-risk map steers the investigation away from pure human focus to include geographic environment, thereby enabling more differentiated non-pharmaceutical interventions and exit strategies to target some places more than others when complete city lockdown is not practicable.
Collapse
Affiliation(s)
- Becky P Y Loo
- Department of Geography, The University of Hong Kong, Pokfulam Road, Pok Fu Lam, Hong Kong
- Institute of Transport Studies, The University of Hong Kong, Pok Fu Lam, Hong Kong
- Urban and Transport Research Laboratory, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Ka Ho Tsoi
- Department of Geography, The University of Hong Kong, Pokfulam Road, Pok Fu Lam, Hong Kong
- Urban and Transport Research Laboratory, The University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Paulina P Y Wong
- Science Unit, Lingnan University of Hong Kong, Pok Fu Lam, Hong Kong
- Centre for Social Policy and Social Change, Lingnan University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Poh Chin Lai
- Department of Geography, The University of Hong Kong, Pokfulam Road, Pok Fu Lam, Hong Kong.
- Institute of Transport Studies, The University of Hong Kong, Pok Fu Lam, Hong Kong.
| |
Collapse
|
10
|
Su J, Sze NN, Bai L. A joint probability model for pedestrian crashes at macroscopic level: Roles of environment, traffic, and population characteristics. ACCIDENT; ANALYSIS AND PREVENTION 2021; 150:105898. [PMID: 33310648 DOI: 10.1016/j.aap.2020.105898] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 11/09/2020] [Accepted: 11/10/2020] [Indexed: 06/12/2023]
Abstract
Road safety is a major public health issue, with road crashes accounting for one-fourth of all documented injuries. In these crashes, pedestrians are more vulnerable to fatal and/or severe injuries than car occupants. Therefore, it is necessary to have a better understanding of the relationship between pedestrian crashes and possible influencing factors, including road environment, traffic conditions, and population characteristics. In conventional studies, separate prediction models were established for pedestrian crashes and other crash types, which could have ignored possible correlations among the different crash types. Additionally, these influencing factors can contribute to pedestrian crashes in two manners, i.e., contributing to crash occurrence and propensity of pedestrian involvement. Furthermore, extensive pedestrian count data were generally not available, affecting the estimation of pedestrian crash exposure. In this study, a joint probability model is adopted for the simultaneous modeling of crash occurrence and pedestrian involvement in crashes; effects of possible influencing factors, including land use, road networks, traffic flow, population demographics and socioeconomics, public transport facilities, and trip attraction attributes, are considered. Additionally, trip generation and pedestrian activity data, based on a comprehensive household travel survey, are used to determine pedestrian crash exposure. Markov chain Monte Carlo full Bayesian approach is then applied to estimate the parameters. Results indicate that crash occurrence is correlated to traffic flow, number of non-signalized intersections, and points of interest such as restaurants and hotels. By contrast, population age, ethnicity, education, household size, road density, and number of public transit stations could affect the propensity of pedestrian involvement in crashes. These findings indicate that better design and planning of built environments are necessary for safe and efficient access for pedestrians and for the long-term improvement of walkability in a high-density city such as Hong Kong.
Collapse
Affiliation(s)
- Junbiao Su
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University Hung Hom, Kowloon, Hong Kong.
| | - N N Sze
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University Hung Hom, Kowloon, Hong Kong.
| | - Lu Bai
- Jiangsu Key Laboratory of Urban ITS, Southeast University Si Pai Lou #2, Nanjing, 210096, China; Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Si Pai Lou #2, Nanjing, 210096, China.
| |
Collapse
|
11
|
Dong N, Meng F, Zhang J, Wong SC, Xu P. Towards activity-based exposure measures in spatial analysis of pedestrian-motor vehicle crashes. ACCIDENT; ANALYSIS AND PREVENTION 2020; 148:105777. [PMID: 33011425 DOI: 10.1016/j.aap.2020.105777] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 08/17/2020] [Accepted: 09/09/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND Although numerous efforts have been devoted to exploring the effects of area-wide factors on the frequency of pedestrian crashes in neighborhoods over the past two decades, existing studies have largely failed to provide a full picture of the factors that contribute to the incidence of zonal pedestrian crashes, due to the unavailability of reliable exposure data and use of less sound analytical methods. METHODS Based on a crowdsourced dataset in Hong Kong, we first proposed a procedure to extract pedestrian trajectories from travel-diary survey data. We then aggregated these data to 209 neighborhoods and developed a Bayesian spatially varying coefficients model to investigate the spatially non-stationary relationships between the number of pedestrian-motor vehicle (PMV) crashes and related risk factors. To dissect the role of pedestrian exposure, the estimated coefficients of models with population, walking trips, walking time, and walking distance as the measure of pedestrian exposure were presented and compared. RESULTS Our results indicated substantial inconsistencies in the effects of several risk factors between the models of population and activity-based exposure measures. The model using walking trips as the measure of pedestrian exposure had the best goodness-of-fit. We also provided new insights that in addition to the unstructured variability, heterogeneity in the effects of explanatory variables on the frequency of PMV crashes could also arise from the spatially correlated effects. After adjusting for vehicle volume and pedestrian activity, road density, intersection density, bus stop density, and the number of parking lots were found to be positively associated with PMV crash frequency, whereas the percentage of motorways and median monthly income had negative associations with the risk of PMV crashes. CONCLUSIONS The use of population or population density as a surrogate for pedestrian exposure when modeling the frequency of zonal pedestrian crashes is expected to produce biased estimations and invalid inferences. Spatial heterogeneity should also not be negligible when modeling pedestrian crashes involving contiguous spatial units.
Collapse
Affiliation(s)
- Ni Dong
- School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan, China; National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu, Sichuan, China; Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, United States
| | - Fanyu Meng
- Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, Shenzhen, China; Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, China
| | - Jie Zhang
- School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, Sichuan, China; National United Engineering Laboratory of Integrated and Intelligent Transportation, Southwest Jiaotong University, Chengdu, Sichuan, China
| | - S C Wong
- Department of Civil Engineering, The University of Hong Kong, Hong Kong, China
| | - Pengpeng Xu
- Department of Civil Engineering, The University of Hong Kong, Hong Kong, China.
| |
Collapse
|
12
|
Kwayu KM, Kwigizile V, Oh JS. Development of systemwide pedestrian safety performance function using stratified random sampling and a proxy measure of pedestrian exposure. Int J Inj Contr Saf Promot 2020; 27:420-431. [PMID: 32654654 DOI: 10.1080/17457300.2020.1791905] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
The lack of pedestrian counts at a systemwide level prompts the need to find other innovative ways of assessing pedestrian traffic crash risks using proxy measures of exposure. This study aims to formulate the methodology for developing pedestrian safety performance functions (SPF) using the proxy measure of pedestrian exposure and stratified random sampling. The case study was all urban intersections in Michigan State that comprise of collector and arterial roads. The stratified random sampling strategy was deployed to select the sample which is representative of all urban intersections in the state of Michigan. Factor analysis was used to develop a proxy measure of pedestrian exposure at urban intersections using a walkability measure (walk score), among other factors. The performances of various count models were compared using the goodness of fit measures based on the Akaike's Information Criterion (AIC), Bayesian Information Criterion (BIC), and Vuong test. The final pedestrian SPFs was formulated using the Zero-Inflated Poisson (ZIP) model with AADT at a major approach, AADT at the minor approach, and a proxy measure of pedestrian exposure. The proposed methodology in this study can benefit transportation agencies that have embarked on systemwide planning of pedestrian facilities to improve the safety of pedestrians but lack systemwide analytical tools and pedestrian counts to make data-driven decisions.
Collapse
Affiliation(s)
- Keneth Morgan Kwayu
- Department of Civil and Construction Engineering, Western Michigan University, Kalamazoo, MI, USA
| | - Valerian Kwigizile
- Department of Civil and Construction Engineering, Western Michigan University, Kalamazoo, MI, USA
| | - Jun-Seok Oh
- Department of Civil and Construction Engineering, Western Michigan University, Kalamazoo, MI, USA
| |
Collapse
|
13
|
Wu J, Xu H, Zhang Y, Sun R. An improved vehicle-pedestrian near-crash identification method with a roadside LiDAR sensor. JOURNAL OF SAFETY RESEARCH 2020; 73:211-224. [PMID: 32563396 DOI: 10.1016/j.jsr.2020.03.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2019] [Revised: 03/03/2020] [Accepted: 03/17/2020] [Indexed: 06/11/2023]
Abstract
PROBLEM Potential conflicts between pedestrians and vehicles represent a challenge to pedestrian safety. Near-crash is used as a surrogate metric for pedestrian safety evaluations when historical vehicle-pedestrian crash data are not available. One challenge of using near-crash data for pedestrian safety evaluation is the identification of near-crash events. METHOD This paper introduces a novel method for pedestrian-vehicle near-crash identification that uses a roadside LiDAR sensor. The trajectory of each road user can be extracted from roadside LiDAR data via several data processing algorithms: background filtering, lane identification, object clustering, object classification, and object tracking. Three indicators, namely, the post encroachment time (PET), the proportion of the stopping distance (PSD), and the crash potential index (CPI) are applied for conflict risk classification. RESULTS The performance of the developed method was evaluated with field-collected data at four sites in Reno, Nevada, United States. The results of case studies demonstrate that pedestrian-vehicle near-crash events could be identified successfully via the proposed method. Practical applications: The proposed method is especially suitable for pedestrian-vehicle near-crash identification at individual sites. The extracted near-crash events can serve as supplementary material to naturalistic driving study (NDS) data for safety evaluation.
Collapse
Affiliation(s)
- Jianqing Wu
- School of Qilu Transportation, Shandong University, China
| | - Hao Xu
- University of Nevada, Reno, Reno, NV 89557, United States
| | - Yongsheng Zhang
- School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China.
| | - Renjuan Sun
- School of Qilu Transportation, Shandong University, China
| |
Collapse
|
14
|
Park S, Ko D. A Multilevel Model Approach for Investigating Individual Accident Characteristics and Neighborhood Environment Characteristics Affecting Pedestrian-Vehicle Crashes. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17093107. [PMID: 32365640 PMCID: PMC7246641 DOI: 10.3390/ijerph17093107] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 04/24/2020] [Accepted: 04/26/2020] [Indexed: 11/21/2022]
Abstract
Walking is the most basic movement of humans and the most fundamental mode of transportation. To promote walking, it is necessary to create a safe environment for pedestrians. However, pedestrian-vehicle crashes still remain relatively high in South Korea. This study employs a multilevel model to examine the differences between the lower-level individual characteristics of pedestrian crashes and the upper-level neighborhood environmental characteristics in Seoul, South Korea. The main results of this study are as follows. The individual characteristics of pedestrian-vehicle crashes are better at explaining pedestrian injury severity than built environment characteristics at the neighborhood level. Older pedestrians and drivers suffer more severe pedestrian injuries. Larger vehicles such as trucks and vans are more likely to result in a high severity of pedestrian injuries. Pedestrian injuries increase during inclement weather and at night. The severity of pedestrian injuries is lower at intersections and crosswalks without traffic signals than at crosswalks and intersections with traffic signals. Finally, school zones and silver zones, which are representative policies for pedestrian safety in South Korea, fail to play a significant role in reducing the severity of pedestrian injuries. The results of this study can guide policymakers and planners when making decisions on how to build neighborhoods that are safer for pedestrians.
Collapse
Affiliation(s)
- Seunghoon Park
- Department of Urban Planning, Keimyung University, Daegu 42601, Korea
- Correspondence: ; Tel.: +82-53-580-5048
| | - Dongwon Ko
- Gyeonggi Research Institute, Suwon 16207, Korea;
| |
Collapse
|
15
|
Feliciani C, Gorrini A, Crociani L, Vizzari G, Nishinari K, Bandini S. Calibration and validation of a simulation model for predicting pedestrian fatalities at unsignalized crosswalks by means of statistical traffic data. JOURNAL OF TRAFFIC AND TRANSPORTATION ENGINEERING (ENGLISH EDITION) 2020. [DOI: 10.1016/j.jtte.2019.01.004] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
16
|
Pljakić M, Jovanović D, Matović B, Mićić S. Macro-level accident modeling in Novi Sad: A spatial regression approach. ACCIDENT; ANALYSIS AND PREVENTION 2019; 132:105259. [PMID: 31454738 DOI: 10.1016/j.aap.2019.105259] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2019] [Revised: 07/10/2019] [Accepted: 07/31/2019] [Indexed: 06/10/2023]
Abstract
In this study, a macroscopic analysis was conducted in order to identify the factors which have an effect on traffic accidents in traffic analysis zones. The factors that impact accidents vary according to the characteristics of the observed area, which in turn leads to a discrepancy between research and practice. The total number of accidents was observed in this paper, along with the number of motorized and non-motorized mode accidents within a three-year period in the city of Novi Sad. The models used for this analysis were spatial predictive models comprised of the classical predictive space model, spatial lag model and spatial error model. The spatial lag model showed the best performances concerning the total number of accidents and number of motorized mode accidents, whereas the spatial error model was prominent within the number of non-motorized mode accidents. The results found that increasing Daily Vehicle-Kilometers Traveled, parking spaces, 5-legged intersections and signalized intersections increased all types of accidents. The other demographic, traffic, road and environment characteristics showed that they had a different effect on the observed types of accidents. The results of this research can be benefitial to reserachers who deal with traffic engineering, space planning as well as making decisions with the aim of preparing countermeasures necessary for road safety improvement in the analysed area.
Collapse
Affiliation(s)
- Miloš Pljakić
- Faculty of Technical Sciences, University of Priština in Kosovska Mitrovica, Serbia
| | - Dragan Jovanović
- Department of Transport and at the Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia.
| | - Boško Matović
- Department of Transport and at the Faculty of Technical Sciences, University of Novi Sad, Novi Sad, Serbia
| | - Spasoje Mićić
- Ministry of Transport and Communications, Republic of Srpska, Bosnia and Herzegovina
| |
Collapse
|
17
|
Investigating the Potential of Using POI and Nighttime Light Data to Map Urban Road Safety at the Micro-Level: A Case in Shanghai, China. SUSTAINABILITY 2019. [DOI: 10.3390/su11174739] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The way in which the occurrence of urban traffic collisions can be conveniently and precisely predicted plays an important role in traffic safety management, which can help ensure urban sustainability. Point of interest (POI) and nighttime light (NTL) data have always been used for characterizing human activities and built environments. By using a district of Shanghai as the study area, this research employed the two types of urban sensing data to map vehicle–pedestrian and vehicle–vehicle collision risks at the micro-level by road type with random forest regression (RFR) models. First, the Network Kernel Density Estimation (NKDE) algorithm was used to generate the traffic collision density surface. Next, by establishing a set of RFR models, the observed density surface was modeled with POI and NTL variables, based on different road types and periods of the day. Finally, the accuracy of the models and the predicted outcomes were analyzed. The results show that the two datasets have great potential for mapping vehicle–pedestrian and vehicle–vehicle collision risks, but they should be carefully utilized for different types of roads and collision types. First, POI and NTL data are not applicable to the modeling of traffic collisions that happen on expressways. Second, the two types of sensing data are quite suitable for estimating the occurrence of traffic collisions on arterial and secondary trunk roads. Third, while the two datasets are capable of predicting vehicle–pedestrian collision risks on branch roads, their ability to predict vehicle safety on branch roads is limited.
Collapse
|
18
|
Identification of Vehicle-Pedestrian Collision Hotspots at the Micro-Level Using Network Kernel Density Estimation and Random Forests: A Case Study in Shanghai, China. SUSTAINABILITY 2018. [DOI: 10.3390/su10124762] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The improvement of pedestrian safety plays a crucial role in developing a safe and friendly walking environments, which can contribute to urban sustainability. A preliminary step in improving pedestrian safety is to identify hazardous road locations for pedestrians. This study proposes a framework for the identification of vehicle-pedestrian collision hot spots by integrating the information about both the likelihood of the occurrence of vehicle-pedestrian collisions and the potential for the reduction in vehicle-pedestrian crashes. First, a vehicle-pedestrian collision density surface was produced via network kernel density estimation. By assigning a threshold value, possible vehicle-pedestrian hot spots were identified. To obtain the potential for vehicle-pedestrian collision reduction, random forests was employed to model the density with a set of variables describing vehicle and pedestrian flows. The potential for crash reduction was then measured as the difference between the observed vehicle-pedestrian crash density and the prediction produced by the random forests models. The final hotspots were determined by excluding those with a crash reduction value of no more than zero. The method was applied to the identification of hazardous road locations for pedestrians in a district in Shanghai, China. The result indicates that the method is useful for decision-making support.
Collapse
|
19
|
Xie SQ, Dong N, Wong SC, Huang H, Xu P. Bayesian approach to model pedestrian crashes at signalized intersections with measurement errors in exposure. ACCIDENT; ANALYSIS AND PREVENTION 2018; 121:285-294. [PMID: 30292868 DOI: 10.1016/j.aap.2018.09.030] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2018] [Revised: 08/23/2018] [Accepted: 09/27/2018] [Indexed: 06/08/2023]
Abstract
This study intended to identify the potential factors contributing to the occurrence of pedestrian crashes at signalized intersections in a densely populated city, based on a comprehensive dataset of 898 pedestrian crashes at 262 signalized intersections during 2010-2012 in Hong Kong. The detailed geometric design, traffic characteristics, signal control, built environment, along with the vehicle and pedestrian volumes were elaborately collected. A Bayesian measurement errors model was introduced as an alternative method to explicitly account for the uncertainties in volume data. To highlight the role played by exposure, models with and without pedestrian volume were estimated and compared. The results indicated that the omission of pedestrian volume in pedestrian crash frequency models would lead to reduced goodness-of-fit, biased parameter estimates, and incorrect inferences. Our empirical analysis demonstrated the existence of moderate uncertainties in pedestrian and vehicle volumes. Six variables were found to have a significant association with the number of pedestrian crashes at signalized intersections. The number of crossing pedestrians, the number of passing vehicles, the presence of curb parking, and the presence of ground-floor shops were positively related with pedestrian crash frequency, whereas the presence of playgrounds near intersections had a negative effect on pedestrian crash occurrences. Specifically, the presence of exclusive pedestrian signals for all crosswalks was found to significantly reduce the risk of pedestrian crashes by 43%. The present study is expected to shed more light on a deeper understanding of the environmental determinants of pedestrian crashes.
Collapse
Affiliation(s)
- S Q Xie
- Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Ni Dong
- School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China
| | - S C Wong
- Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Helai Huang
- School of Traffic and Transportation Engineering, Central South University, Changsha, China
| | - Pengpeng Xu
- Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China.
| |
Collapse
|
20
|
Fan Y, Zhu X, She B, Guo W, Guo T. Network-constrained spatio-temporal clustering analysis of traffic collisions in Jianghan District of Wuhan, China. PLoS One 2018; 13:e0195093. [PMID: 29672551 PMCID: PMC5909624 DOI: 10.1371/journal.pone.0195093] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2017] [Accepted: 03/18/2018] [Indexed: 11/19/2022] Open
Abstract
The analysis of traffic collisions is essential for urban safety and the sustainable development of the urban environment. Reducing the road traffic injuries and the financial losses caused by collisions is the most important goal of traffic management. In addition, traffic collisions are a major cause of traffic congestion, which is a serious issue that affects everyone in the society. Therefore, traffic collision analysis is essential for all parties, including drivers, pedestrians, and traffic officers, to understand the road risks at a finer spatio-temporal scale. However, traffic collisions in the urban context are dynamic and complex. Thus, it is important to detect how the collision hotspots evolve over time through spatio-temporal clustering analysis. In addition, traffic collisions are not isolated events in space. The characteristics of the traffic collisions and their surrounding locations also present an influence of the clusters. This work tries to explore the spatio-temporal clustering patterns of traffic collisions by combining a set of network-constrained methods. These methods were tested using the traffic collision data in Jianghan District of Wuhan, China. The results demonstrated that these methods offer different perspectives of the spatio-temporal clustering patterns. The weighted network kernel density estimation provides an intuitive way to incorporate attribute information. The network cross K-function shows that there are varying clustering tendencies between traffic collisions and different types of POIs. The proposed network differential Local Moran’s I and network local indicators of mobility association provide straightforward and quantitative measures of the hotspot changes. This case study shows that these methods could help researchers, practitioners, and policy-makers to better understand the spatio-temporal clustering patterns of traffic collisions.
Collapse
Affiliation(s)
- Yaxin Fan
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
| | - Xinyan Zhu
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
- * E-mail: (XZ); (BZ)
| | - Bing She
- China Data Center, University of Michigan, Ann Arbor, United States of America
- * E-mail: (XZ); (BZ)
| | - Wei Guo
- State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China
| | - Tao Guo
- Wuhan Digital Engineering Research Institute, Wuhan, China
| |
Collapse
|
21
|
Culyba AJ, Guo W, Branas CC, Miller E, Wiebe DJ. Comparing residence-based to actual path-based methods for defining adolescents' environmental exposures using granular spatial data. Health Place 2017; 49:39-49. [PMID: 29190517 DOI: 10.1016/j.healthplace.2017.11.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2017] [Revised: 10/23/2017] [Accepted: 11/20/2017] [Indexed: 11/27/2022]
Abstract
This paper uses data from a population-based case control study of daily activities and assault injury to examine residence-based versus actual path-based approaches to measuring environmental exposures that pose risks for violence among adolescents. Defining environmental exposures based on participant home address resulted in significant misclassification compared to gold standard daily travel path measures. Dividing participant daily travel paths into origin-destination segments, we explore a method for defining spatial counterfactuals by comparing actual trip path exposures to shortest potential trip path exposures. Spatial methods explored herein can be utilized in future research to more accurately quantify environmental exposures and associations with health outcomes.
Collapse
Affiliation(s)
- Alison J Culyba
- Craig-Dalsimer Division of Adolescent Medicine, The Children's Hospital of Philadelphia, 3401 Civic Center Boulevard, Philadelphia, PA 19104, United States; Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, United States.
| | - Wensheng Guo
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, United States.
| | - Charles C Branas
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, United States.
| | - Elizabeth Miller
- Division of Adolescent and Young Adult Medicine, Children's Hospital of Pittsburgh, Oakland Medical Building, 3420 Fifth Avenue, Pittsburgh, PA 15213, United States.
| | - Douglas J Wiebe
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, United States.
| |
Collapse
|
22
|
Quistberg DA, Howard EJ, Hurvitz PM, Moudon AV, Ebel BE, Rivara FP, Saelens BE. The Relationship Between Objectively Measured Walking and Risk of Pedestrian-Motor Vehicle Collision. Am J Epidemiol 2017; 185:810-821. [PMID: 28338921 PMCID: PMC5411678 DOI: 10.1093/aje/kwx020] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2015] [Revised: 07/11/2016] [Accepted: 07/26/2016] [Indexed: 12/21/2022] Open
Abstract
Safe urban walking environments may improve health by encouraging physical activity, but the relationship between an individual's location and walking pattern and the risk of pedestrian-motor vehicle collision is unknown. We examined associations between individuals' walking bouts and walking risk, measured as mean exposure to the risk of pedestrian-vehicle collision. Walking bouts were ascertained through integrated accelerometry and global positioning system data and from individual travel-diary data obtained from adults in the Travel Assessment and Community Study (King County, Washington) in 2008-2009. Walking patterns were superimposed onto maps of the historical probabilities of pedestrian-vehicle collisions for intersections and midblock segments within Seattle, Washington. Mean risk of pedestrian-vehicle collision in specific walking locations was assessed according to walking exposure (duration, distance, and intensity) and participant demographic characteristics in linear mixed models. Participants typically walked in areas with low pedestrian collision risk when walking for recreation, walking at a faster pace, or taking longer-duration walks. Mean daily walking duration and distance were not associated with collision risk. Males walked in areas with higher collision risk compared with females, while vehicle owners, residents of single-family homes, and parents of young children walked in areas with lower collision risk. These findings may suggest that pedestrians moderate collision risk by using lower-risk routes.
Collapse
Affiliation(s)
- D. Alex Quistberg
- Correspondence to Dr. D. Alex Quistberg, Urban Health Collaborative, Department of Environmental and Occupational Health, Dornsife School of Public Health, Drexel University, 3215 Market Street, Philadelphia, PA 19104 (e-mail: )
| | | | | | | | | | | | | |
Collapse
|
23
|
Guo Q, Xu P, Pei X, Wong SC, Yao D. The effect of road network patterns on pedestrian safety: A zone-based Bayesian spatial modeling approach. ACCIDENT; ANALYSIS AND PREVENTION 2017; 99:114-124. [PMID: 27894026 DOI: 10.1016/j.aap.2016.11.002] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2016] [Revised: 10/08/2016] [Accepted: 11/03/2016] [Indexed: 06/06/2023]
Abstract
Pedestrian safety is increasingly recognized as a major public health concern. Extensive safety studies have been conducted to examine the influence of multiple variables on the occurrence of pedestrian-vehicle crashes. However, the explicit relationship between pedestrian safety and road network characteristics remains unknown. This study particularly focused on the role of different road network patterns on the occurrence of crashes involving pedestrians. A global integration index via space syntax was introduced to quantify the topological structures of road networks. The Bayesian Poisson-lognormal (PLN) models with conditional autoregressive (CAR) prior were then developed via three different proximity structures: contiguity, geometry-centroid distance, and road network connectivity. The models were also compared with the PLN counterpart without spatial correlation effects. The analysis was based on a comprehensive crash dataset from 131 selected traffic analysis zones in Hong Kong. The results indicated that higher global integration was associated with more pedestrian-vehicle crashes; the irregular pattern network was proved to be safest in terms of pedestrian crash occurrences, whereas the grid pattern was the least safe; the CAR model with a neighborhood structure based on road network connectivity was found to outperform in model goodness-of-fit, implying the importance of accurately accounting for spatial correlation when modeling spatially aggregated crash data.
Collapse
Affiliation(s)
- Qiang Guo
- Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - Pengpeng Xu
- Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong.
| | - Xin Pei
- Department of Automation, Tsinghua University, Beijing, 100084, China.
| | - S C Wong
- Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong.
| | - Danya Yao
- Department of Automation, Tsinghua University, Beijing, 100084, China.
| |
Collapse
|
24
|
Xu P, Huang H, Dong N, Wong SC. Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach. ACCIDENT; ANALYSIS AND PREVENTION 2017; 98:330-337. [PMID: 27816012 DOI: 10.1016/j.aap.2016.10.015] [Citation(s) in RCA: 43] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2016] [Revised: 09/08/2016] [Accepted: 10/11/2016] [Indexed: 06/06/2023]
Abstract
This study was performed to investigate the spatially varying relationships between crash frequency and related risk factors. A Bayesian spatially varying coefficients model was elaborately introduced as a methodological alternative to simultaneously account for the unstructured and spatially structured heterogeneity of the regression coefficients in predicting crash frequencies. The proposed method was appealing in that the parameters were modeled via a conditional autoregressive prior distribution, which involved a single set of random effects and a spatial correlation parameter with extreme values corresponding to pure unstructured or pure spatially correlated random effects. A case study using a three-year crash dataset from the Hillsborough County, Florida, was conducted to illustrate the proposed model. Empirical analysis confirmed the presence of both unstructured and spatially correlated variations in the effects of contributory factors on severe crash occurrences. The findings also suggested that ignoring spatially structured heterogeneity may result in biased parameter estimates and incorrect inferences, while assuming the regression coefficients to be spatially clustered only is probably subject to the issue of over-smoothness.
Collapse
Affiliation(s)
- Pengpeng Xu
- Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| | - Helai Huang
- School of Traffic & Transportation Engineering, Central South University, Changsha, Hunan, China.
| | - Ni Dong
- School of Transportation & Logistics, Southwest Jiaotong University, Chengdu, Sichuan, China
| | - S C Wong
- Department of Civil Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
| |
Collapse
|
25
|
Yang BZ, Loo BPY. Land use and traffic collisions: A link-attribute analysis using Empirical Bayes method. ACCIDENT; ANALYSIS AND PREVENTION 2016; 95:236-249. [PMID: 27454868 DOI: 10.1016/j.aap.2016.07.002] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2016] [Revised: 05/30/2016] [Accepted: 07/04/2016] [Indexed: 06/06/2023]
Abstract
Road traffic collisions represent one of the major public health problems among the leading causes of deaths globally. This paper examines several approaches in detecting hazardous road locations, and discusses the spatial distribution of these locations as well as their relationships with different land uses in Hong Kong. Two most commonly used methodologies in detecting hazardous road locations are used: the hot spot and hot zone methodologies. Both methodologies are performed using raw collision count, excess collision count and Empirical Bayes (EB) estimations. The EB estimation uses land use characteristics near the road network in defining the reference groups. Finally all the approaches are compared by a test to assess their stability. The results show that for different hazardous road location detection methodologies, the best fit estimation methods on sites are different. The results confirm some land use impacts in previous studies, and suggest some further patterns on road safety. The findings are useful in understanding the complex interrelationships between land use and road safety, and in facilitating planners and policy makers to build safer cities.
Collapse
Affiliation(s)
- Bruce Zi Yang
- Department of Geography, The University of Hong Kong, Hong Kong, China.
| | - Becky P Y Loo
- Department of Geography, The University of Hong Kong, Hong Kong, China.
| |
Collapse
|
26
|
Yao S, Loo BPY. Safety in numbers for cyclists beyond national-level and city-level data: a study on the non-linearity of risk within the city of Hong Kong. Inj Prev 2016; 22:379-385. [PMID: 27339061 PMCID: PMC5256166 DOI: 10.1136/injuryprev-2016-041964] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Revised: 05/23/2016] [Accepted: 05/24/2016] [Indexed: 11/04/2022]
Abstract
OBJECTIVE This paper examines the relationship between bicycle collisions and the amount of cycling at the local level. Most previous research has focused on national and city comparisons, little is known about differences within a city (the mesoscale). METHODS This study mainly used three types of data sets relating to bicycle collisions, use of bicycles and local neighbourhood characteristics in Hong Kong. In particular, bicycle usage, measured as bicycle-kilometres travelled, was estimated from travel surveys following the activity-based approach. Negative binomial regression models were established to model the relationship between the amount of cycling and the occurrence of bicycle collisions at the spatial scale of the Tertiary Planning Unit, which is the smallest planning unit of the city. RESULTS The numbers of bicycle collisions went up with the increasing use of bicycles, but the increase in the number of collisions in a given community was less than a linear proportion of the bicycle flow. When other local neighbourhood variables are controlled, the amount of cycling is a statistically significant variable in accounting for the number of collisions. CONCLUSIONS Even in a highly motorised city where bicycles are a minor transport mode, cyclists are less likely to be involved in road collisions in communities with higher cycling volume. Since cycling activities are likely to vary within a city, a more local-based approach in promoting cycling is needed. In particular, the higher safety risks in neighbourhoods of low bicycle usage, especially at an initial stage of promoting cycling, need to be addressed properly.
Collapse
Affiliation(s)
- Shenjun Yao
- Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai, China
| | - Becky P Y Loo
- Department of Geography, The University of Hong Kong, Hong Kong, Hong Kong
| |
Collapse
|
27
|
Rhee KA, Kim JK, Lee YI, Ulfarsson GF. Spatial regression analysis of traffic crashes in Seoul. ACCIDENT; ANALYSIS AND PREVENTION 2016; 91:190-199. [PMID: 26994374 DOI: 10.1016/j.aap.2016.02.023] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2015] [Revised: 08/26/2015] [Accepted: 02/26/2016] [Indexed: 06/05/2023]
Abstract
Traffic crashes can be spatially correlated events and the analysis of the distribution of traffic crash frequency requires evaluation of parameters that reflect spatial properties and correlation. Typically this spatial aspect of crash data is not used in everyday practice by planning agencies and this contributes to a gap between research and practice. A database of traffic crashes in Seoul, Korea, in 2010 was developed at the traffic analysis zone (TAZ) level with a number of GIS developed spatial variables. Practical spatial models using available software were estimated. The spatial error model was determined to be better than the spatial lag model and an ordinary least squares baseline regression. A geographically weighted regression model provided useful insights about localization of effects. The results found that an increased length of roads with speed limit below 30 km/h and a higher ratio of residents below age of 15 were correlated with lower traffic crash frequency, while a higher ratio of residents who moved to the TAZ, more vehicle-kilometers traveled, and a greater number of access points with speed limit difference between side roads and mainline above 30 km/h all increased the number of traffic crashes. This suggests, for example, that better control or design for merging lower speed roads with higher speed roads is important. A key result is that the length of bus-only center lanes had the largest effect on increasing traffic crashes. This is important as bus-only center lanes with bus stop islands have been increasingly used to improve transit times. Hence the potential negative safety impacts of such systems need to be studied further and mitigated through improved design of pedestrian access to center bus stop islands.
Collapse
Affiliation(s)
- Kyoung-Ah Rhee
- Graduate School of Environmental Studies, Seoul National University, Main Campus, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea.
| | - Joon-Ki Kim
- Korea Research Institute for Human Settlements, National Infrastructure Research Division, 224 Simin-daero, Dongan-gu, Anyang-si, Gyeonggi-do 14067, Republic of Korea.
| | - Young-ihn Lee
- Graduate School of Environmental Studies, Seoul National University, Main Campus, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea.
| | - Gudmundur F Ulfarsson
- University of Iceland, Faculty of Civil and Environmental Engineering, Hjardarhagi 2-6, IS-107 Reykjavik, Iceland.
| |
Collapse
|
28
|
Kim DH, Ramjan LM, Mak KK. Prediction of vehicle crashes by drivers' characteristics and past traffic violations in Korea using a zero-inflated negative binomial model. TRAFFIC INJURY PREVENTION 2015; 17:86-90. [PMID: 26043956 DOI: 10.1080/15389588.2015.1033689] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
AIMS Traffic safety is a significant public health challenge, and vehicle crashes account for the majority of injuries. This study aims to identify whether drivers' characteristics and past traffic violations may predict vehicle crashes in Korea. METHODS A total of 500,000 drivers were randomly selected from the 11.6 million driver records of the Ministry of Land, Transport and Maritime Affairs in Korea. Records of traffic crashes were obtained from the archives of the Korea Insurance Development Institute. After matching the past violation history for the period 2004-2005 with the number of crashes in year 2006, a total of 488,139 observations were used for the analysis. Zero-inflated negative binomial model was used to determine the incident risk ratio (IRR) of vehicle crashes by past violations of individual drivers. The included covariates were driver's age, gender, district of residence, vehicle choice, and driving experience. RESULTS Drivers violating (1) a hit-and-run or drunk driving regulation at least once and (2) a signal, central line, or speed regulation more than once had a higher risk of a vehicle crash with respective IRRs of 1.06 and 1.15. Furthermore, female gender, a younger age, fewer years of driving experience, and middle-sized vehicles were all significantly associated with a higher likelihood of vehicle crashes. CONCLUSIONS Drivers' demographic characteristics and past traffic violations could predict vehicle crashes in Korea. Greater resources should be assigned to the provision of traffic safety education programs for the high-risk driver groups.
Collapse
Affiliation(s)
- Dae-Hwan Kim
- a Department of Economics , Dong-A University , Busan , Republic of Korea
| | - Lucie M Ramjan
- b School of Nursing and Midwifery, University of Western Sydney , Australia
- c Centre for Applied Nursing Research (CANR), Ingham Institute of Applied Medical Research , Sydney , Australia
| | - Kwok-Kei Mak
- d Department of Psychology , University of Hong Kong , Hong Kong
| |
Collapse
|
29
|
Characterization of Black Spot Zones for Vulnerable Road Users in São Paulo (Brazil) and Rome (Italy). ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2015. [DOI: 10.3390/ijgi4020858] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
|