Road traffic crashes and risk groups in India: Analysis, interpretations, and prevention strategies

Ponnaluri, Raj

doi:10.1016/j.iatssr.2011.09.002

Cited by 49 publications

(24 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Also, the area covered by a police station is limited and they keep record of accidents that have occurred in their regions only. Ponnaluri [7] discussed that the report prepared by police only contains the basic information that are not much useful for the research purpose. He suggests that data collection method used by police needs a lot of improvement.…”

Section: Introductionmentioning

confidence: 99%

A data mining approach to characterize road accident locations

2016

View full text Add to dashboard Cite

Data mining has been proven as a reliable technique to analyze road accidents and provide productive results. Most of the road accident data analysis use data mining techniques, focusing on identifying factors that affect the severity of an accident. However, any damage resulting from road accidents is always unacceptable in terms of health, property damage and other economic factors. Sometimes, it is found that road accident occurrences are more frequent at certain specific locations. The analysis of these locations can help in identifying certain road accident features that make a road accident to occur frequently in these locations. Association rule mining is one of the popular data mining techniques that identify the correlation in various attributes of road accident. In this paper, we first applied k-means algorithm to group the accident locations into three categories, high-frequency, moderate-frequency and low-frequency accident locations. k-means algorithm takes accident frequency count as a parameter to cluster the locations. Then we used association rule mining to characterize these locations. The rules revealed different factors associated with road accidents at different locations with varying accident frequencies. The association rules for high-frequency accident location disclosed that intersections on highways are more dangerous for every type of accidents. High-frequency accident locations mostly involved two-wheeler accidents at hilly regions. In moderate-frequency accident locations, colonies near local roads and intersection on highway roads are found dangerous for pedestrian hit accidents. Low-frequency accident locations are scattered throughout the district and the most of the accidents at these locations were not critical. Although the data set was limited to some selected attributes, our approach extracted some useful hidden information from the data which can be utilized to take some preventive efforts in these locations.

show abstract

Section: Introductionmentioning

confidence: 99%

A data mining approach to characterize road accident locations

2016

View full text Add to dashboard Cite

show abstract

“…In India, several road traffic crash studies [5][6][7] have been done but only a few applied Smeed's law or other methods, especially for developing region-specific or time-dependent models; Valli [8], for example, developed models for metropolitan cities. The main purpose of this work is to examine the relevance of Smeed's law to India and to develop time-and state-specific models.…”

Section: Introductionmentioning

confidence: 99%

Modeling road traffic fatalities in India: Smeed's law, time invariance and regional specificity

Ponnaluri

2012

IATSS Research

Self Cite

View full text Add to dashboard Cite

Mathematical formulations linking road traffic fatalities to vehicle ownership, regional population, and economic growth continue to be developed against the backdrop of Smeed and Andreassen models. Though a few attempts were made, Smeed's law has not been fully tested in India. Using the 1991-2009 panel data from all states, this work (a) developed the generalized Smeed and Andreassen models; (b) evaluated if traffic fatalities were impacted by structural changes; and (c) examined if -in relation to the generalized model -the individual (time and regional) models are more relevant for application. Seven models (Smeed: original, generalized, time-variant, state-variant; and Andreassen: generalized, time-variant, state-variant) were developed and tested for fit with the actual data. Results showed that the per vehicle fatality rate closely resembled Smeed's formulation. Chow-test yielded a significant F-stat, suggesting that the models for four pre-defined time-blocks are structurally different from the 19-year generalized model. The counterclockwise rotation of the log-linear form also suggested lower fatality rates. While the new government policies, reduced vehicle operating speeds, better healthcare, and improved vehicle technology could be the factors, further research is required to understand the reasons for fatality rate reductions. The intercept and gradients of the time-series models showed high stability and varied only slightly in comparison to the 19-year generalized models, thus suggesting that the latter are pragmatic for application. Regional formulations, however, indicate that they may be more relevant for studying trends and tendencies. This research illustrates the robustness of Smeed's law, and provides evidence for timeinvariance but state-specificity.

show abstract

“…Karim et al 11 noted that the frequency and scale of vehicle overloading are severe in Malaysia, and Pinard15 cites statistics showing that about one in five vehicles weighed in routine weighbridge operation in KwaZulu-Natal (South Africa) are overloaded. In India, more than a quarter of fatalities in 2001 and 2009 were attributed to overloading 16. These high levels of overloading are generally attributed to economic benefit for drivers and the lack of enforcement.…”

Section: Introductionmentioning

confidence: 99%