Investigating Spatial Autocorrelation and Spillover Effects in Freeway Crash-Frequency Data

This study attempts to investigate spatial autocorrelation and spillover effects in micro traffic safety analysis. To achieve the objective, a Poisson-based count regression with consideration of these spatial effects is proposed for modeling crash frequency on freeway segments. In the proposed hybrid model, the spatial autocorrelation and the spillover effects are formulated as the conditional autoregressive (CAR) prior and the exogenous variables of adjacent segments, respectively. The proposed model is demonstrated and compared to the models with only one kind of spatial effect, using one-year crash data collected from Kaiyang Freeway, China. The results of Bayesian estimation conducted in WinBUGS show that significant spatial autocorrelation and spillover effects simultaneously exist in the freeway crash-frequency data. The lower value of deviance information criterion (DIC) and more significant exogenous variables for the hybrid model compared to the other alternatives, indicate the strength of accounting for both spatial autocorrelation and spillover effects on improving model fit and identifying crash contributing factors. Moreover, the model results highlight the importance of daily vehicle kilometers traveled, and horizontal and vertical alignments of targeted segments and adjacent segments on freeway crash occurrences.

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Road traffic accidents on Senai-Desaru expressway

MATEC Web of Conferences ◽

10.1051/matecconf/201825002002 ◽

2018 ◽

Vol 250 ◽

pp. 02002 ◽

Cited By ~ 1

Author(s):

Nordiana Mashros ◽

SittiAsmah Hassan ◽

Yaacob Haryati ◽

Mohd Shahrir Amin Ahmad ◽

Ismail Samat ◽

...

Keyword(s):

Traffic Safety ◽

Traffic Accidents ◽

Road Traffic ◽

Contributing Factors ◽

Property Damage ◽

Crash Frequency ◽

Crash Data ◽

Run Off ◽

Accident Data ◽

Road Segments

Understanding and prioritising crash contributing factors is important for improving traffic safety on the expressway. This paper aims to identify the possible contributory factors that were based on findings obtained from crash data at Senai-Desaru Expressway (SDE), which is the main connector between the western and eastern parts of Johor, Malaysia. Using reported accident data, the mishaps that had occurred along the 77.2 km road were used to identify crash patterns and their possible related segment conditions. The Average Crash Frequency and Equivalent Property Damage Only Average Crash Frequency Methods had been used to identify and rank accident-prone road segments as well as to propose for appropriate simple and inexpensive countermeasures. The results show that the dominant crash type along the road stretches of SDE had consisted of run-off-road collision and property damage only crashes. All types of accidents were more likely to occur during daytime. Out of the 154 segments, the 4 most accident-prone road segments had been determined and analysed. The results obtained from the analyses suggest that accident types are necessary for identifying the possible causes of accidents and the appropriate strategies for countermeasures. Therefore, this accident analysis could be helpful to relevant authorities in reducing the number of road accidents and the level of accident severity along the SDE.

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The Effects of Traffic Composition on Freeway Crash Frequency by Injury Severity: A Bayesian Multivariate Spatial Modeling Approach

Journal of Advanced Transportation ◽

10.1155/2018/6964828 ◽

2018 ◽

Vol 2018 ◽

pp. 1-7 ◽

Cited By ~ 10

Author(s):

Huiying Wen ◽

Jiaren Sun ◽

Qiang Zeng ◽

Xuan Zhang ◽

Quan Yuan

Keyword(s):

Injury Severity ◽

Spatial Effects ◽

Crash Frequency ◽

Safety Improvement ◽

Proposed Model ◽

Conditional Autoregressive ◽

Heavy Truck ◽

Traffic Composition ◽

Adjacent Segments ◽

Horizontal Curvature

This study sets out to investigate the effects of traffic composition on freeway crash frequency by injury severity. A crash dataset collected from Kaiyang Freeway, China, is adopted for the empirical analysis, where vehicles are divided into five categories and crashes are classified into no injury and injury levels. In consideration of correlated spatial effects between adjacent segments, a Bayesian multivariate conditional autoregressive model is proposed to link no-injury and injury crash frequencies to the risk factors, including the percentages of different vehicle categories, daily vehicle kilometers traveled (DVKT), and roadway geometry. The model estimation results show that, compared to Category 5 vehicles (e.g., heavy truck), larger percentages of Categories 1 (e.g., passenger car) and 3 (e.g., medium truck) vehicles would lead to less no-injury crashes and more injury crashes. DVKT, horizontal curvature, and vertical grade are also found to be associated with no-injury and/or injury crash frequencies. The significant heterogeneous and spatial effects for no-injury and injury crashes justify the applicability of the proposed model. The findings are helpful to understand the relationship between traffic composition and freeway safety and to provide suggestions for designing strategies of vehicle safety improvement.

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Regional Disparity and Patients Mobility: Benefits and Spillover Effects of the Spatial Network Structure of the Health Services in China

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18031096 ◽

2021 ◽

Vol 18 (3) ◽

pp. 1096

Author(s):

Liping Fu ◽

Kaibo Xu ◽

Feng Liu ◽

Lu Liang ◽

Zhengmin Wang

Keyword(s):

Health Economic ◽

Health Services ◽

Network Structure ◽

Spillover Effect ◽

Spillover Effects ◽

Health Economy ◽

Spatial Network ◽

Spatial Effects ◽

Medical Resource ◽

Medical Resources

Background: The distribution of medical resources in China is seriously imbalanced due to imbalanced economic development in the country; unbalanced distribution of medical resources makes patients try to seek better health services. Against this backdrop, this study aims to analyze the spatial network characteristics and spatial effects of China’s health economy, and then find evidence that affects patient mobility. Methods: Data for this study were drawn from the China Health Statistical Yearbooks and China Statistical Books. The gravitational value of China’s health spatial network was calculated to establish a network of gravitational relationships. The social network analysis method was used for centrality analysis and spillover effect analysis. Results: A gravity correlation matrix was constructed among provinces by calculating the gravitational value, indicating the spatial relationships of different provinces in the health economic network. Economically developed provinces, such as Shanghai and Jiangsu, are at the center of the health economic network (centrality degree = 93.333). These provinces also play a strong intermediary role in the network and have connections with other provinces. In the CONCOR analysis, 31 provinces are divided into four blocks. The spillover effect of the blocks indicates provinces with medical resource centers have beneficial effects, while provinces with insufficient resources have obvious spillover effects. Conclusion: There is a significant gap in the geographical distribution of medical resources, and the health economic spatial network structure needs to be improved. Most medical resources are concentrated in economically developed provinces, and these provinces’ positions in the health economic spatial network are becoming more centralized. By contrast, economically underdeveloped regions are at the edge of the network, causing patients to move to provinces with medical resource centers. There are health risks of the increasing pressure to seek medical treatment in developed provinces with abundant medical resources.

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Identifying Temporal Aggregation Effect on Crash-Frequency Modeling

Sustainability ◽

10.3390/su13116214 ◽

2021 ◽

Vol 13 (11) ◽

pp. 6214

Author(s):

Bumjoon Bae ◽

Changju Lee ◽

Tae-Young Pak ◽

Sunghoon Lee

Keyword(s):

Traffic Safety ◽

Negative Binomial ◽

Random Effect ◽

Temporal Correlation ◽

Negative Binomial Regression ◽

Spatiotemporal Analysis ◽

Parameter Estimates ◽

Crash Frequency ◽

Crash Frequency Modeling ◽

Using Data

Aggregation of spatiotemporal data can encounter potential information loss or distort attributes via individual observation, which would influence modeling results and lead to an erroneous inference, named the ecological fallacy. Therefore, deciding spatial and temporal resolution is a fundamental consideration in a spatiotemporal analysis. The modifiable temporal unit problem (MTUP) occurs when using data that is temporally aggregated. While consideration of the spatial dimension has been increasingly studied, the counterpart, a temporal unit, is rarely considered, particularly in the traffic safety modeling field. The purpose of this research is to identify the MTUP effect in crash-frequency modeling using data with various temporal scales. A sensitivity analysis framework is adopted with four negative binomial regression models and four random effect negative binomial models having yearly, quarterly, monthly, and weekly temporal units. As the different temporal unit was applied, the result of the model estimation also changed in terms of the mean and significance of the parameter estimates. Increasing temporal correlation due to using the small temporal unit can be handled with the random effect models.

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Integrated Transport Efficiency and Its Spatial Convergence in China’s Provinces: A Super-SBM DEA Model Considering Undesirable Outputs

Applied Sciences ◽

10.3390/app8091698 ◽

2018 ◽

Vol 8 (9) ◽

pp. 1698 ◽

Cited By ~ 6

Author(s):

Fei Ma ◽

Wenlin Wang ◽

Qipeng Sun ◽

Fei Liu ◽

Xiaodan Li

Keyword(s):

Spatial Autocorrelation ◽

Traffic Accidents ◽

Central Zone ◽

Data Envelopment Analysis Model ◽

Undesirable Outputs ◽

Spatial Effects ◽

Analysis Model ◽

Growth Trend ◽

Transport Efficiency ◽

Transport Industry

Undesirable outputs, such as carbon emissions and loss of property due to traffic accidents, hold great significance for the sustainable development of the transport industry. In this study, we applied a super-efficiency data envelopment analysis model with a slack-based measure (Super-SBM DEA) considering undesirable outputs to measure the integrated transport efficiency (ITE) of 31 provinces in China during the period of 2009–2016. Following this, we used a spatial autocorrelation model to test and verify the spatial autocorrelation of the ITEs at the level of the 31 provinces, and further to explore the aggregating features. Finally, considering the spatial effects that emerged, we constructed a β-convergence model to analyze the convergence characteristics of China’s ITEs and investigate its conditional factors. The research results show that the average ITE demonstrated a linear growth trend; the effective decision-making units (the ITE value was greater than 1) are only 11 provinces, accounting for about 35% by 2016. The mean of ITEs was also found to present a law of decreasing order of Eastern, Central and Western Zones. However, the Central Zone and Western Zone have a better efficiency improvement trend compared to the Eastern Zone. The Moran’s I index was bigger than zero, indicating that the ITEs formed a spatial autocorrelation phenomenon. The Moran scatter plots further showed that the provincial ITEs mainly followed the patterns of high–high, high–low and low–low aggregation. The ITE of the 31 provinces was found to have a clear absolute β-convergence and conditional β-convergence characteristics. Moreover, the level of economic development, household per capita traffic consumption, transport industry scale, technology advancement and transport intensity were all seen to have an important impact on the convergence of integrated transport efficiency. It is hoped that the findings of this study may contribute further insights and practical knowledge to effectively measuring the development level of China’s integrated transport efficiency, and to understanding future changes in the ITE gap among Chinese provinces.

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Evaluation of Automated Enforcement Program in Amman

Periodica Polytechnica Transportation Engineering ◽

10.3311/pptr.10939 ◽

2018 ◽

Author(s):

Hana Naghawi ◽

Bushra Al Qatawneh ◽

Rabab Al Louzi

Keyword(s):

Traffic Safety ◽

Questionnaire Survey ◽

Positive Impact ◽

Red Light ◽

Crash Frequency ◽

Red Light Running ◽

Red Light Cameras ◽

Percent Confidence Level ◽

Before And After ◽

Traffic Violations

This study aims, in a first attempt, to evaluate the effectiveness of using the Automated Enforcement Program (AEP) to improve traffic safety in Amman, Jordan. The evaluation of the program on crashes and violations was examined based on a “before-and-after” study using the paired t-test at 95 percent confidence level. Twenty one locations including signalized intersections monitored by red light cameras and arterial roads monitored by excessive speed cameras were selected. Nine locations were used to study the effectiveness of the program on violations, and twelve locations were used to determine the effectiveness of the program on frequency and severity of crashes. Data on number and severity of crashes were taken from Jordan Traffic Institution. Among the general findings, it was found that the AEP was generally associated with positive impact on crashes. Crash frequency was significantly reduced by up to 63%. Crash severities were reduced by up to 62.5%. Also, traffic violations were significantly reduced by up to 66%. Finally, drivers’ opinion and attitude on the program was also analyzed using a questionnaire survey. The questionnaire survey revealed that 35.5% of drivers are unaware of AEP in Amman, 63.9% of drivers don’t know the camera locations, most drivers knew about excessive speed and red light running penalties, most drivers reduce their speed at camera locations, 44.4% of drivers think that the program satisfies its objective in improving traffic safety and 52% of drivers encourage increasing the number of camera devices in Amman.

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Pooling in Tourism Demand Forecasting

Journal of Travel Research ◽

10.1177/0047287518800390 ◽

2018 ◽

Vol 58 (7) ◽

pp. 1161-1174 ◽

Cited By ~ 7

Author(s):

Wen Long ◽

Chang Liu ◽

Haiyan Song

Keyword(s):

Panel Data ◽

Fixed Effects ◽

Demand Forecasting ◽

Spillover Effects ◽

Ordinary Least Squares ◽

Spatial Effects ◽

Tourism Demand ◽

Forecasting Accuracy ◽

Demand Models ◽

Forecasting Performance

This study investigates whether pooling can improve the forecasting performance of tourism demand models. The short-term domestic tourism demand forecasts for 341 cities in China using panel data (pooled) models are compared with individual ordinary least squares (OLS) and naïve benchmark models. The pooled OLS model demonstrates much worse forecasting performance than the other models. This indicates the huge heterogeneity of tourism across cities in China. A marked improvement with the inclusion of fixed effects suggests that destination features that stay the same or vary very little over time can explain most of the heterogeneity. Adding spatial effects to the panel data models also increases forecasting accuracy, although the improvement is small. The spatial distribution of spillover effects is drawn on a map and a spatial pattern is recognized. Finally, when both spatial and temporal effects are taken into account, pooling improves forecasting performance.

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Spatial Effects and Ecological Inference

Political Analysis ◽

10.1093/pan/10.3.276 ◽

2002 ◽

Vol 10 (3) ◽

pp. 276-297 ◽

Cited By ~ 62

Author(s):

Luc Anselin ◽

Wendy K. Tam Cho

Keyword(s):

Data Analysis ◽

Spatial Autocorrelation ◽

Random Coefficient ◽

Parameter Estimates ◽

Spatial Effects ◽

Ecological Inference ◽

Simulation Experiments ◽

Coefficient Variation ◽

Inference Methods

This paper examines the role of spatial effects in ecological inference. Both formally and through simulation experiments, we consider the problems associated with ecological inference and cross-level inference methods in the presence of increasing degrees of spatial autocorrelation. Past assessments of spatial autocorrelation in aggregate data analysis focused on unidimensional, one-directional processes that are not representative of the full complexities caused by spatial autocorrelation. Our analysis is more complete and representative of true forms of spatial autocorrelation and pays particular attention to the specification of spatial autocorrelation in models with random coefficient variation. Our assessment focuses on the effects of this specification on the bias and precision of parameter estimates.

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