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<p>Algorithmic monitoring, such as electronic police devices, and human policing are both commonly used to monitor traffic violations and increase compliance with traffic rules. To compare the influence of algorithmic monitoring with that of human monitoring on compliance, two studies involving scenario-based experiments and field observations were conducted. Our findings reveal that for traffic violation behaviors with low construal (e.g., speeding), individuals exhibited a greater degree of fit (i.e., feeling right) between the monitored behavior and algorithmic monitoring, thereby increasing their intention to comply with traffic rules. However, this effect was absent when errors occurred in algorithmic monitoring. Conversely, for traffic violation behaviors with high construal (e.g., failure to yield right-of-way), individuals demonstrated stronger intentions to comply with traffic rules, even when monitoring errors were highlighted. This relationship is mediated by the extent of fit between the monitoring agents and their monitored violations. Hence, the effectiveness of algorithmic monitoring over human monitoring in promoting compliance depends on the construal of traffic behavior, with algorithmic errors weakening its effectiveness for low-construal traffic behaviors. Therefore, when selecting a monitoring agent, traffic departments should consider the levels at which observed traffic violations at an intersection are construed rather than indiscriminately applying either algorithmic or human monitoring.</p>

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Construal level of traffic violation behavior: comparing the impacts of algorithmic monitoring and human monitoring on traffic compliance

  • 期刊论文

  • 发表日期：

    2025年6月13日

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  • Qinglu Xiao,
  • Shiqi Wang,
  • Jiakai Chen,
  • Wanqiu Xu,
  • Lingxia Fan,
  • Jun Yin

摘要

Algorithmic monitoring, such as electronic police devices, and human policing are both commonly used to monitor traffic violations and increase compliance with traffic rules. To compare the influence of algorithmic monitoring with that of human monitoring on compliance, two studies involving scenario-based experiments and field observations were conducted. Our findings reveal that for traffic violation behaviors with low construal (e.g., speeding), individuals exhibited a greater degree of fit (i.e., feeling right) between the monitored behavior and algorithmic monitoring, thereby increasing their intention to comply with traffic rules. However, this effect was absent when errors occurred in algorithmic monitoring. Conversely, for traffic violation behaviors with high construal (e.g., failure to yield right-of-way), individuals demonstrated stronger intentions to comply with traffic rules, even when monitoring errors were highlighted. This relationship is mediated by the extent of fit between the monitoring agents and their monitored violations. Hence, the effectiveness of algorithmic monitoring over human monitoring in promoting compliance depends on the construal of traffic behavior, with algorithmic errors weakening its effectiveness for low-construal traffic behaviors. Therefore, when selecting a monitoring agent, traffic departments should consider the levels at which observed traffic violations at an intersection are construed rather than indiscriminately applying either algorithmic or human monitoring.