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<p>An efficient Bus Rapid Transit System (BRTS) can significantly mitigate challenges related to travel time (TT) in conventional public transportation. Among the key indicators of transit service quality are Travel Time Variability (TTV) and Travel Time Reliability (TTR), both of which are influenced by numerous spatial and temporal factors that affect overall system performance. However, comprehensive studies using Intelligent Transportation System (ITS) data particularly GPS-based Automatic Vehicle Location (AVL) and Automated Passenger Count (APC) remain limited due to historical data unavailability. This study addresses that gap by leveraging high-resolution AVL and APC data from the Hubli-Dharwad BRTS (HDBRTS), India, to examine TTV and TTR patterns across express/non-express routes and dedicated/non-dedicated segments. The analysis was conducted in three stages. In the first stage, TTV characteristics were analyzed using descriptive statistics and fitted with seven continuous probability distributions, with the Generalized Extreme Value (GEV) distribution emerging as the most robust across spatial levels. The second stage involved modeling TTR using Multiple Linear Regression (MLR), incorporating both operator and passenger perspectives. Average Travel Time (ATT) and Buffer Time (BT) served as dependent variables, while explanatory variables were selected based on strong Pearson correlation coefficients. The models yielded adjusted R2 values of 0.795 and 0.804, respectively, with segment-level passenger demand showing the strongest positive influence. The third stage explored the relationship between GEV shape parameter (k), Buffer Time Index (BTI), and hourly passenger demand, revealing that variations in demand and BTI are closely associated with changes in distribution shape, thereby explaining transit reliability across space and time. This data-driven approach provides empirical insights into evaluating performance within the studied BRT corridor and offers context-specific evidence relevant to reliability assessment under similar operational conditions.</p>

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Investigating travel time variability and reliability of a bus rapid transit system: a case study using ITS-based AVL and APC data

  • 期刊论文

  • 发表日期：

    2026年4月16日

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  • Shivaraj Halyal,
  • Raviraj H. Mulangi

摘要

An efficient Bus Rapid Transit System (BRTS) can significantly mitigate challenges related to travel time (TT) in conventional public transportation. Among the key indicators of transit service quality are Travel Time Variability (TTV) and Travel Time Reliability (TTR), both of which are influenced by numerous spatial and temporal factors that affect overall system performance. However, comprehensive studies using Intelligent Transportation System (ITS) data particularly GPS-based Automatic Vehicle Location (AVL) and Automated Passenger Count (APC) remain limited due to historical data unavailability. This study addresses that gap by leveraging high-resolution AVL and APC data from the Hubli-Dharwad BRTS (HDBRTS), India, to examine TTV and TTR patterns across express/non-express routes and dedicated/non-dedicated segments. The analysis was conducted in three stages. In the first stage, TTV characteristics were analyzed using descriptive statistics and fitted with seven continuous probability distributions, with the Generalized Extreme Value (GEV) distribution emerging as the most robust across spatial levels. The second stage involved modeling TTR using Multiple Linear Regression (MLR), incorporating both operator and passenger perspectives. Average Travel Time (ATT) and Buffer Time (BT) served as dependent variables, while explanatory variables were selected based on strong Pearson correlation coefficients. The models yielded adjusted R2 values of 0.795 and 0.804, respectively, with segment-level passenger demand showing the strongest positive influence. The third stage explored the relationship between GEV shape parameter (k), Buffer Time Index (BTI), and hourly passenger demand, revealing that variations in demand and BTI are closely associated with changes in distribution shape, thereby explaining transit reliability across space and time. This data-driven approach provides empirical insights into evaluating performance within the studied BRT corridor and offers context-specific evidence relevant to reliability assessment under similar operational conditions.