Unveiling the retardant effectiveness of node-based driving restriction policies in mitigating congestion

• Node-based driving restrictions (NDRs) demonstrate retardant effectiveness in relieving congestion. • A retardant model was developed to analyze these effects and validated using the case of Chongqing, China. • Flexible traffic patterns have a moderating impact on NDRs’ retardant effects. • Our findings support wider NDR adoption to optimize restrictions in the context of dynamic travel patterns. Traffic congestion continues to pose a significant challenge to sustainable urbanization, prompting many cities to implement driving restriction policies. Node-based driving restrictions (NDRs), designed to regulate vehicle movements through critical nodes in a transportation network, have emerged as a popular and effective strategy for alleviating congestion. Although numerous efforts have been made to examine the essence of NDRs, their mechanism and overall effectiveness remains unclear. By drawing an analogy to the behavior of flame retardants in composite material combustion, this study explores the effectiveness of NDRs and the conditions necessary to optimize their impact. A retardant model was proposed and tested using the case of Chongqing, China. The Baidu Traffic API data were analyzed employing difference-in-differences (DID) and triple differences (DDD) methods. The results indicate that NDRs exert retardant effects by temporarily reducing congestion peaks. NDRs improve vehicle flow within the network, shorten vehicle queues, and limit the spread of congestion. Furthermore, congestion resulting from flexible travel patterns is more susceptible to the impacts of NDRs, suggesting that assessments of NDRs’ effectiveness should consider the moderating effect of flexible travel behaviors. These findings advocate for the broader validation and implementation of NDRs, highlighting their cost-effectiveness and adaptability in developing flexible congestion mitigation strategies. They also provide novel insights into congestion reduction and offer guidance for optimizing restriction policies in dynamic travel patterns.