The most crucial factor to consider when analysing the performance of signalised intersection is delay. However, measuring delays is difficult, particularly in a heterogeneous and less lane disciplined traffic that prevails in developing nations. Hence, for the determination of traffic delay from the field, theoretical delay equations are used. The most commonly used theoretical delay equations such as Webster’s delay equation [1], HCM equation [2], and Ackelick’s equation [3] are derived for the homogeneous and lane based traffic condition. There are only limited studies on this from the heterogeneous and lane less traffic conditions [4–6]. The current study evaluates the performance of some of these models under heterogeneous traffic conditions using calibrated SUMO network. Results showed the need for models to be developed specifically for different traffic conditions for realistic performance. The best performing model was evaluated using field data and the average errors were 3.69 s/PCU of Mean Absolute Error and 17.33% of Mean Absolute Percentage Error indicating the selected delay model to be a promising one to be used under heterogeneous and lane less traffic conditions.

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Evaluation of Theoretical Delay Models for Heterogeneous Traffic Conditions

  • Chithra A. Saikrishna,
  • Abdhul Khadhir,
  • Lelitha Vanajakshi

摘要

The most crucial factor to consider when analysing the performance of signalised intersection is delay. However, measuring delays is difficult, particularly in a heterogeneous and less lane disciplined traffic that prevails in developing nations. Hence, for the determination of traffic delay from the field, theoretical delay equations are used. The most commonly used theoretical delay equations such as Webster’s delay equation [1], HCM equation [2], and Ackelick’s equation [3] are derived for the homogeneous and lane based traffic condition. There are only limited studies on this from the heterogeneous and lane less traffic conditions [4–6]. The current study evaluates the performance of some of these models under heterogeneous traffic conditions using calibrated SUMO network. Results showed the need for models to be developed specifically for different traffic conditions for realistic performance. The best performing model was evaluated using field data and the average errors were 3.69 s/PCU of Mean Absolute Error and 17.33% of Mean Absolute Percentage Error indicating the selected delay model to be a promising one to be used under heterogeneous and lane less traffic conditions.