A Data-Driven Assessment and Prioritization of Urban Road Accident Burden in Indian Cities for Transportation Safety

Urban road-safety planning requires a transparent basis for identifying where engineering, enforcement and publicawareness resources may be prioritized. This study develops a reproducible framework to assess reported road accident burden across 50 Indian million-plus cities using official statistics. The 2023 city-level dataset contains 81,144 reported accidents, 17,255 fatalities and 73,476 injuries. The framework combines aggregate trend analysis, entropy-weighted Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), K-means clustering, ranking sensitivity analysis and a two-axis burden– severity matrix. Entropy weighting assigns weights of 0.3412, 0.2695 and 0.3894 to accidents, fatalities and injuries, respectively. Delhi, Bengaluru and Jabalpur are the three highest-ranked cities under the composite burden score. A five-cluster solution yields a silhouette coefficient of 0.4387 and mean pairwise adjusted Rand index of 0.9929 across 20 initializations. Rank agreement is high under equal, accident-focused and injury-focused weights, but lower when fatalities receive dominant emphasis. The results show that city prioritization depends on the selected outcome dimension. The framework is a screeninglevel decision-support approach: because consistent city-specific exposure denominators are unavailable, the results represent reported burden and must not be interpreted as exposure-adjusted crash risk or causal evidence.