Urban traffic emissions and the rise of digital behavioural interventions

Urban transport systems are closely linked to some of the most pressing environmental challenges facing cities today, including air pollution, fossil fuel dependence and declining environmental quality. Road traffic remains a dominant source of urban greenhouse gas (GHG) emissions and atmospheric pollutants worldwide. Global transport-related CO₂ emissions reached c. 8.0 Gt in 2022, representing c. 20–24% of total energy-related emissions, with road transport dominating the sector by contributing 70–75% of total transport emissions (International Energy Agency 2023). Urban traffic is also a major source of nitrogen oxides (NOₓ), particulate matter (PM) and other air pollutants, contributing significantly to public health risks and environmental degradation (Fazakas et al. Reference Fazakas, Neamtiu and Gurzau2024).

Conventional approaches to reducing transport-related environmental impacts have largely focused on technological and infrastructural transitions, including vehicle electrification, fuel efficiency improvements and stricter emission standards (Wang et al. Reference Wang, Dong, Zhang and Wang2024b). While these strategies are indispensable for long-term decarbonization, their implementation is often constrained by high capital costs, infrastructure requirements and relatively slow adoption rates. This has led to increasing interest in complementary approaches that can deliver near-term benefits without requiring large-scale physical transformations.

Among these complementary strategies, digital behavioural interventions are receiving increasing attention because they use real-time information to influence individual decisions without necessarily requiring large-scale changes in physical infrastructure (Meuleners et al. Reference Meuleners, Fraser, Stevenson and Roberts2023, Liang & Chen Reference Liang and Chen2026). Traffic signal countdown systems illustrate this approach by enabling drivers to anticipate signal changes and adjust acceleration, braking and idling behaviour accordingly (Pan et al. Reference Pan, Yang, Zhang, Ma, Yang and Zhang2023, Zhang et al. Reference Zhang, Fu, Xu and Wang2025). Previous studies have examined such systems primarily from the perspectives of driving behaviour, traffic efficiency, safety, eco-driving, fuel consumption and vehicle emissions under specific traffic or technological conditions. However, these findings remain relatively fragmented from an environmental management perspective. In particular, less attention has been given to how signal countdown information delivered through widely used mobile navigation platforms could function as a scalable behavioural intervention, how its environmental benefits depend on implementation context and how it might complement rather than substitute for longer-term technological and infrastructural transitions.

This Comment addresses this gap by synthesizing existing evidence on countdown-informed driving and reframing it within the broader context of urban environmental management. We focus on three linked questions: how real-time signal information can alter transient driving behaviour relevant to energy use and emissions; what environmental and operational benefits have been reported in previous field and simulation studies; and which behavioural, technological, governance, safety and accessibility factors may determine whether these benefits can be realized in practice. Through this synthesis, we use mobile traffic signal countdown information as a specific example to illustrate how existing digital infrastructure may support behaviour-based emission mitigation and complement wider strategies for sustainable urban mobility.

Conceptually, mobile traffic signal countdown information can be represented as an information–behaviour–operation–environment pathway (Fig. 1). By reducing uncertainty and improving anticipation, signal information may alter acceleration, deceleration and idling, thereby smoothing vehicle operation and potentially lowering energy use and emissions. These effects may contribute to broader sustainability benefits, but each linkage depends on traffic and signal conditions, driver characteristics and compliance, interface design, data reliability and accessibility.

Figure 1.

Conceptual framework linking mobile traffic signal countdown information to potential urban environmental outcomes. Countdown information may influence driver perception and anticipation, driving behaviour, vehicle operation and, ultimately, energy use and emissions, with broader implications for urban sustainability. The strength of these linkages is moderated by traffic conditions, signal control systems, driver characteristics and compliance, interface design, data reliability and accessibility. The pathway to lower non-exhaust particulate matter (PM) is supported indirectly through effects on driving behaviour on brake and tyre wear and has not yet been directly quantified for mobile signal countdown systems. GHG = greenhouse gas.

Figure 1. Long description

Panel 1: Signal countdown information. Real-time red/green phase information displayed on a device. Panel 2: Perception and anticipation. A head with an eye indicating reduced uncertainty and improved awareness of upcoming signal changes. Panel 3: Driving behaviour. A driver with smoother deceleration, less unnecessary acceleration, and reduced idling. Panel 4: Vehicle operation. A car with smoother speed trajectories, fewer high-power events, and lower fuel/energy demand. Panel 5: Environmental outcomes. A cloud with CO₂ indicating lower fuel consumption, reduced greenhouse gas and air-pollutant emissions, and potentially lower non-exhaust particulate matter. Panel 6: Urban sustainability implications. Buildings and trees indicating improved environmental efficiency of existing mobility systems and complementary support for sustainable urban mobility. Moderating conditions include traffic density, signal coordination/control, driver compliance, interface design, data accuracy/reliability, safety, and accessibility.

Signal countdown information reshapes urban driving dynamics

A critical yet often-underappreciated aspect of urban traffic emissions lies in their strong dependence on transient driving conditions. Unlike highway driving, which is characterized by relatively stable speeds, urban driving involves frequent interruptions caused by signalized intersections, congestion and vehicle interactions, resulting in repeated cycles of acceleration, deceleration and idling, each of which contributes to elevated fuel consumption and emission rates (Tu et al. Reference Tu, Xu, Wang, Zhang, Zhai and Hatzopoulou2022). Real-world transient driving tests indicate that aggressive driving behaviours (i.e., typical of stop-and-go urban traffic) can increase carbon monoxide (CO) emissions by up to c. 88% and PM emissions by more than 110% relative to smoother driving patterns (Shahariar et al. Reference Shahariar, Bodisco, Zare, Sajjad, Jahirul and Chu Van2022), highlighting the amplified environmental burden associated with non-steady driving. Among these behaviours, abrupt acceleration following red-light stops produces short-term peaks in fuel consumption and exhaust emissions, contributing to localized pollution hotspots around intersections (Zhao et al. Reference Zhao, Jia, Wei, Guo, Yao and Yin2023).

Such transient inefficiencies can be interpreted as systemic losses arising from suboptimal operational behaviour. Just as industrial processes can be optimized to reduce energy waste and emissions through better control strategies, urban traffic systems can be improved by influencing the micro-level decisions of drivers. In this context, the provision of real-time information plays a crucial role. When drivers lack visibility of signal timing, they are forced to react to changes rather than anticipate them, leading to abrupt manoeuvres and inefficient energy use. Conversely, when reliable timing information is available, it becomes possible to smooth driving trajectories and reduce unnecessary kinetic fluctuations.

The introduction of traffic signal countdown information into mobile navigation applications directly addresses this information gap. By displaying the remaining duration of red or green phases, these systems enable drivers to anticipate upcoming changes and adjust their behaviour accordingly (Pan et al. Reference Pan, Yang, Zhang, Ma, Yang and Zhang2023, Zhang et al. Reference Zhang, Fu, Xu and Wang2025). This seemingly simple intervention has profound implications for driving dynamics. When approaching an intersection, a driver who knows that a red light will persist for several seconds is more likely to decelerate gradually rather than brake abruptly. Similarly, awareness of an imminent signal change can discourage attempts to accelerate aggressively in order to pass through an intersection during the final moments of a green phase (Fig. 2). Over time, such adjustments lead to smoother speed profiles, reduced stop-and-go frequency and lower reliance on high-power acceleration events.

Figure 2.

Example of a mobile navigation interface displaying real-time traffic signal countdown information at a signalized urban intersection. The visualization provides drivers with the remaining green or red phase duration, enabling anticipatory driving behaviours such as smoother deceleration, reduced idling and avoidance of unnecessary acceleration. By decreasing stop-and-go driving and improving intersection throughput, this digital intervention has the potential to enhance traffic flow efficiency and reduce vehicle fuel consumption and exhaust emissions in urban road networks. CO = carbon monoxide; NO_x = nitrogen oxides; PM_2.5 = particulate matter with a diameter of 2.5 µm or less.

Field observations and simulation studies indicate that countdown-informed driving can reduce fuel use and traffic-related emissions under specific conditions, but the reported effects span different analytical scales, from individual vehicle operating modes to multi-intersection traffic systems (Kim & Kim Reference Kim and Kim2020, Wang et al. Reference Wang, Guo, Wang and Guo2024a, Di et al. Reference Di, Zhang, Babayi, Zhou and Zhang2025; full details in Appendix S1 Table S1 & Text S1). Accordingly, these estimates are strongly context- and scale-dependent and should not be directly extrapolated to corridor-, network- or city-wide emission reductions.

Potential environmental and operational co-benefits of smoother eco-driving

One of the major advantages of countdown-informed driving is that it can be implemented rapidly and at relatively low cost by leveraging existing digital infrastructures. Because mobile navigation platforms are already widely used in urban environments, the integration of countdown information can be implemented with minimal additional infrastructure. This contrasts sharply with measures such as road expansion, vehicle replacement or large-scale electrification, all of which require significant investment and time. From a cleaner mobility management standpoint, the ability to achieve system-level efficiency gains without increasing material or energy inputs represents a particularly attractive feature. In essence, the intervention enhances the performance of the existing system rather than replacing it.

Smoother driving may also provide co-benefits for non-exhaust particulate emissions, although the evidence supporting this pathway is indirect and should be interpreted cautiously. Signal countdown information can affect braking behaviour: in a driving simulator study, green signal countdown timers increased stopping probability and reduced average deceleration rates, indicating gentler braking under the tested conditions (Islam et al. Reference Islam, Wyman and Hurwitz2017). Independent evidence further shows that driving style can influence wear-related emissions. Real-world low-emission driving behaviour reduced brake-wear PM_2.5 and PM₁₀ emissions by 23% and 26%, respectively (Liu et al. Reference Liu, Wei, Chen, Wu, Tang and Lin2025), while a long-term real-world study identified braking and acceleration events as important behavioural determinants of tyre wear (Liu et al. Reference Liu, Chen, Wu, Gao, Li and An2022). Together, these findings support a plausible pathway whereby countdown-enabled smoother driving could reduce brake and tyre wear and associated non-exhaust PM. However, direct measurements linking mobile traffic signal countdown information itself to brake-wear or tyre-wear particulate emissions remain limited. We therefore regard reduced non-exhaust PM as a potential indirect co-benefit rather than an established outcome, and targeted field studies simultaneously measuring countdown use, driving dynamics, component wear and particle emissions are needed to quantify this pathway.

At the corridor or network scale, the cumulative effects of smoother individual driving may extend beyond direct vehicle-level fuel and emission changes. In principle, reducing abrupt acceleration, braking and stop-and-go disturbances could improve traffic flow stability and intersection throughput, with possible secondary reductions in congestion-related emissions. However, these broader effects should be regarded as system-level implications rather than direct extrapolations of the vehicle- or intersection-scale percentage reductions cited earlier. Their magnitude will depend on factors such as traffic demand, signal coordination, adoption of countdown-informed driving, fleet composition and interactions among vehicles, and therefore they require dedicated corridor- or network-scale evaluation.

Implications for sustainable urban mobility and smart transportation

Despite its potential advantages, the effectiveness of countdown-informed driving depends on behavioural, technological and accessibility conditions. Driver responses are unlikely to be uniform: drivers receptive to eco-driving cues may use countdown information to decelerate earlier, whereas more risk-prone or time-sensitive drivers may accelerate to ‘catch’ a green signal. Interface design should therefore encourage anticipatory rather than speed-seeking behaviour. Reliable synchronization with signal control systems and equitable access to countdown information are also important for translating behavioural responses into environmental benefits .

From a policy perspective, integrating real-time traffic signal countdown information into mobile navigation provides an example of how digitalization may complement technological and infrastructural approaches to managing urban mobility. Rather than requiring major physical system replacement, it uses information and behavioural adaptation to improve the efficiency of existing mobility systems. Integration with connected and automated vehicles may further enable signal information to support algorithmic speed trajectory optimization, although its environmental performance will remain context-dependent (Appendix S1 Text S2).

Traffic signal countdown information illustrates how existing digital infrastructure may support behaviour-based urban emission mitigation. Existing evidence suggests that improved signal anticipation can promote smoother driving and reduce energy use and emissions under specific conditions, but the evidence remains heterogeneous and scale-dependent. Vehicle- and intersection-level reductions cannot be directly extrapolated to network- or city-scale benefits, while driver heterogeneity, traffic conditions, information reliability, interface design, accessibility and indirectly supported co-benefits such as non-exhaust PM further constrain generalization.

Future research should test the complete information–behaviour–operation–environment pathway under real-world conditions by jointly measuring countdown use, driver response, vehicle activity, energy demand and emissions across heterogeneous users and vehicle types. Corridor- and network-scale studies should incorporate realistic adoption, signal coordination, congestion and mixed traffic, while human-centred evaluations should address safety, interface design, reliability and accessibility. Such evidence is needed to determine where countdown-informed driving can meaningfully complement broader sustainable mobility strategies.